Automation doi: 10.3390/automation5020004
Authors: Cristian Berceanu Monica Pătrașcu
In the original publication [...]
]]>Automation doi: 10.3390/automation5010003
Authors: David Schackmann Esther Bosch
With increasing automation in the rail sector, the train driver’s task changes from full control to a supervisory position. This bears the risk of monotony and subsequent changes in visual attention, possibly for the worse. Similar to concepts in car driving, one solution for this could be driver state monitoring with triggered interventions in case of declining task attention. Previous research on train drivers’ visual attention has used eye tracking. In contrast, head tracking is easier to realize within the train driver cabin. This study set out to test whether head tracking is a feasible alternative to eye tracking and can provide similar findings. Based on previous eye-tracking research, we compared differences in head movements in automated vs. manual driving, and for different levels of driving speed and driving experience. We conducted a study with 25 active train drivers in a high-fidelity train simulator. Statistical analyses revealed no significant difference in the vertical head movements between automation levels. There was a significant difference in the horizontal head movements, with train drivers looking more to the right for manual driving. We found no significant influence of driving speed and experience on head movements. Safety implications and the feasibility of head tracking as an alternative to eye tracking are discussed.
]]>Automation doi: 10.3390/automation5010002
Authors: Khaled H. Mahmoud G. T. Abdel-Jaber Abdel-Nasser Sharkawy
In this paper, the aim is to classify torque signals that are received from a 3-DOF manipulator using a pattern recognition neural network (PR-NN). The output signals of the proposed PR-NN classifier model are classified into four indicators. The first predicts that no collisions occur. The other three indicators predict collisions on the three links of the manipulator. The input data to train the PR-NN model are the values of torque exerted by the joints. The output of the model predicts and identifies the link on which the collision occurs. In our previous work, the position data for a 3-DOF robot were used to estimate the external collision torques exerted by the joints when applying collisions on each link, based on a recurrent neural network (RNN). The estimated external torques were used to design the current PR-NN model. In this work, the PR-NN model, while training, could successfully classify 56,592 samples out of 56,619 samples. Thus, the model achieved overall effectiveness (accuracy) in classifying collisions on the robot of 99.95%, which is almost 100%. The sensitivity of the model in detecting collisions on the links “Link 1, Link 2, and Link 3” was 97.9%, 99.7%, and 99.9%, respectively. The overall effectiveness of the trained model is presented and compared with other previous entries from the literature.
]]>Automation doi: 10.3390/automation5010001
Authors: Marco Ullrich Rashik Thalappully Frieder Heieck Bernd Lüdemann-Ravit
Various software environments have been developed in the past to create digital twins of single cells or a digital twin of a factory. Each environment has its own strengths and weaknesses and has been designed with a specific focus. The environments that are able to holistically simulate complete factories are limited in terms of the modelling details required for the analysis of single manufacturing cells (e.g., manufacturer-independence of the individual digital twins) and their ability for virtual commissioning. This paper presents three options for realising a virtual commissioning of linked cells using a 3D integration platform with NVIDIA Omniverse, consisting of two different digital models fused into a combined model, also representing material flow. First, with a source/sink solution and unidirectional connector controlled by OPC UA; secondly, with a bidirectional connector, developed in the course of this elaboration, and an extension of the 3D integration platform controlled by Apache Kafka; thirdly, with a bidirectional connector and using only an extension of the 3D integration platform. The research demonstrates that virtually commissioning multiple linked digital twins from different manufacturers in a 3D platform with material flow makes a significant contribution to the industrial metaverse.
]]>Automation doi: 10.3390/automation4040022
Authors: João Pedro T. Andrade Pedro Leon F. C. Bazan Vivian S. Medeiros Alan C. Kubrusly
Ultrasonic waves generated and received by electromagnetic acoustic transducers (EMATs) are advantageous in non-destructive testing, mainly due to the ability to operate without physical contact with the medium under test. Nevertheless, they present a main drawback of less efficiency, which leads to a lower signal-to-noise ratio. To overcome this, the L-network impedance-matching network is often used in order to ensure maximum power transfer to the EMAT from the excitation electronics. There is a wide range of factors that affect an EMAT’s impedance, apart from the transducer itself; namely, the properties of the specimen material, temperature, and frequency. Therefore, to ensure optimal power transfer, the matching network’s configuration needs to be fine-tuned often. Therefore, the automation of the laborious process of manually adjusting the network is of great benefit to the use of EMAT transducers. In this work, a simplified one-parallel-element automatic matching network is proposed and its theoretical optimal value is derived. Next, an automatic matching network was designed and fabricated. Experiments were performed with two different EMATs at several frequencies obtaining good agreement with theoretical predictions. The automatic system was able to determine the best configuration for the one-element matching network and provided up to 5.6 dB gain, similar to a standard manual solution and considerably faster.
]]>Automation doi: 10.3390/automation4040021
Authors: Natalia Hartono F. Javier Ramírez Duc Truong Pham
In a circular economy, strategies for product recovery, such as reuse, recycling, and remanufacturing, play an important role at the end of a product’s life. A sustainability model was developed to solve the problem of sequence-dependent robotic disassembly line balancing. This research aimed to assess the viability of the model, which was optimised using the Multi-Objective Bees Algorithm in a robotic disassembly setting. Two industrial gear pumps were used as case studies. Four objectives (maximising profit, energy savings, emissions reductions and minimising line imbalance) were set. Several product recovery scenarios were developed to find the best recovery plans for each component. An efficient metaheuristic, the Bees Algorithm, was used to find the best solution. The robotic disassembly plans were generated and assigned to robotic workstations simultaneously. Using the proposed sustainability model on end-of-life industrial gear pumps shows the applicability of the model to real-world problems. The Multi-Objective Bees Algorithm was able to find the best scenario for product recovery by assigning each component to recycling, reuse, remanufacturing, or disposal. The performance of the algorithm is consistent, producing a similar performance for all sustainable strategies. This study addresses issues that arise with product recovery options for end-of-life products and provides optimal solutions through case studies.
]]>Automation doi: 10.3390/automation4040020
Authors: Lukas Christ Elías Milloch Marius Boshoff Alfred Hypki Bernd Kuhlenkötter
Increasing volatility in manufacturing and rising sustainability requirements demand more efficient processes in production, especially in employee qualification and engineering during development and on-site adjustments before and after the start of production. One possible solution is using digital twins for virtual commissioning, which can speed up engineering processes, qualify employees, and save valuable resources. To solve these challenges, it is necessary to identify promising approaches for using the digital twin and virtual commissioning. Furthermore, creating an environment where these approaches can be optimally explored is essential. This paper presents promising research approaches and demonstrates the development of an assembly process and a production system with a digital twin designed to explore these aspects. The presented system is an interlinked production system for assembling an actual industrial product. It includes different levels of human–robot interaction and automation, which can be implemented virtually in the digital twin.
]]>Automation doi: 10.3390/automation4040019
Authors: Ming-Hung Hung Chao-Hsun Ku Kai-Ying Chen
In recent years, with the rise of the automation wave, reducing manual judgment, especially in defect detection in factories, has become crucial. The automation of image recognition has emerged as a significant challenge. However, the problem of how to effectively improve the classification of defect detection and the accuracy of the mean average precision (mAP) is a continuous process of improvement and has evolved from the original visual inspection of defects to the present deep learning detection system. This paper presents an application of deep learning, and the task-aligned approach is firstly used on metal defects, and the anchor and bounding box of objects and categories are continuously optimized by mutual correction. We used the task-aligned one-stage object detection (TOOD) model, then improved and optimized it, followed by deformable ConvNets v2 (DCNv2) to adjust the deformable convolution, and finally used soft efficient non-maximum suppression (Soft-NMS) to optimize intersection over union (IoU) and adjust the IoU threshold and many other experiments. In the Northeastern University surface defect detection dataset (NEU-DET) for surface defect detection, mAP increased from 75.4% to 77.9%, a 2.5% increase in mAP, and mAP was also improved compared to existing advanced models, which has potential for future use.
]]>Automation doi: 10.3390/automation4040018
Authors: Enrico Mendez Javier Piña Camacho Jesús Arturo Escobedo Cabello Alfonso Gómez-Espinosa
In order to improve agriculture productivity, autonomous navigation algorithms are being developed so that robots can navigate along agricultural environments to automatize tasks that are currently performed by hand. This work uses machine vision techniques such as the Otsu’s method, blob detection, and pixel counting to detect the center of the row. Additionally, a commutable control is implemented to autonomously navigate a vineyard. Experimental trials were conducted in an actual vineyard to validate the algorithm. In these trials show that the algorithm can successfully guide the robot through the row without any collisions. This algorithm offers a computationally efficient solution for vineyard row navigation, employing a 2D camera and the Otsu’s thresholding technique to ensure collision-free operation.
]]>Automation doi: 10.3390/automation4030017
Authors: Arman Fathollahi Meysam Gheisarnejad Jalil Boudjadar Sayed Yaser Derakhshandeh Mohammad Hassan Khooban
In this paper, a new design strategy is developed for the Wireless Charging Electric Transit Bus (WCETB). The technology is innovative in that the battery in the bus is charged while it is moving over the charging infrastructure. In particular, an improved version of the Whale Optimization Algorithm (IWOA) is adopted for the WCETB system in the road map of Wakefield City, located in the United Kingdom. The main challenge in the WCETB is to select the power transmitter and battery size efficiently from an economical point of view. For this purpose, both factors are considered in the objective function to achieve the benefits of WCETBs from an energy perspective. Two analytical economic design optimization models are developed in this work. The first model is the real- environment model, which considers a WCETB system operating under typical traffic conditions characterized by vehicle interactions and inherent uncertainties. In this scenario, vehicle speeds vary with time, and specific traffic routes may encounter congestion. The second model concentrates on a WCETB system operating in a traffic-free environment with minimal vehicle interactions and uncertainties. The IWOA is implemented for the WCETB to operate in the real environment. Under traffic-free environment conditions, we utilize mathematical procedures and General Algebraic Modeling System (GAMS) software to solve the optimization problem. This approach not only allows us to comprehensively analyze the WCETB system’s behavior but also examine the interactions among different components of the objective function and constraints. Finally, a comprehensive numerical analysis under various conditions, including changes in the number of buses and increases in the length of routes, is conducted.
]]>Automation doi: 10.3390/automation4030016
Authors: Abdel-Nasser Sharkawy
This paper proposes and implements an approach to evaluate human–robot cooperation aimed at achieving high performance. Both the human arm and the manipulator are modeled as a closed kinematic chain. The proposed task performance criterion is based on the condition number of this closed kinematic chain. The robot end-effector is guided by the human operator via an admittance controller to complete a straight-line segment motion, which is the desired task. The best location of the selected task is determined by maximizing the minimum of the condition number along the path. The performance of the proposed approach is evaluated using a criterion related to ergonomics. The experiments are executed with several subjects using a KUKA LWR robot to repeat the specified motion to evaluate the introduced approach. A comparison is presented between the current proposed approach and our previously implemented approach where the task performance criterion was based on the manipulability index of the closed kinematic chain. The results reveal that the condition number-based approach improves the human–robot cooperation in terms of the achieved accuracy, stability, and human comfort, but at the expense of task speed and completion time. On the other hand, the manipulability-index-based approach improves the human–robot cooperation in terms of task speed and human comfort, but at the cost of the achieved accuracy.
]]>Automation doi: 10.3390/automation4030015
Authors: Jiachen Song Jianguo Guo Changtao Qin Wanliang Zhao
The reaction flywheel is a crucial operational component within a satellite’s attitude control system. Enhancing the performance of the reaction flywheel speed control system holds significant importance for satellite attitude control. In this paper, an active disturbance rejection control (ADRC) approach is introduced to mitigate the impact of uncertain disturbances on reaction flywheel speed control precision. The reaction flywheel speed control system is designed as an ADRC controller due to the current challenge of measuring unknown disturbances accurately in the reaction flywheel system. To derive the rotor’s speed observation value and the estimated total disturbances value, the sampled data of the reaction flywheel rotor position and torque control signal are fed into the extended state observer. The estimated total disturbances value is compensated on feedforward control, which could mitigate significantly the effects of various nonlinear disturbances. The paper initially establishes the rationale behind the reaction flywheel ADRC controller through theoretical analysis, followed by analysis of the differences of performance of reaction flywheel control by the ADRC controller and the PID controller in MATLAB/SIMULINK. Simulation results demonstrate the evident advantages of the ADRC controller over the PID controller in terms of speed command tracking capability and disturbances suppression ability. Subsequently, the ADRC controller program and the PID controller program are implemented on the reaction flywheel control circuit, and experiments are conducted to contrast speed command tracking and disturbance suppression. Importantly, the experimental outcomes align with the simulation results.
]]>Automation doi: 10.3390/automation4030014
Authors: Terje Solsvik Kristensen Asgeir H. Sognefest
Financial markets are complex, evolving dynamic systems. Due to their irregularity, financial time series forecasting is regarded as a rather challenging task. In recent years, artificial neural network applications in finance for such tasks as pattern recognition, classification, and time series forecasting have dramatically increased. The objective of this paper is to present this versatile framework and attempt to use it to predict the stock return series of four public-listed companies on the New York Stock Exchange. Our findings coincide with those of Burton Malkiel in his book, A Random Walk Down Wall Street; no conclusive evidence is found that our proposed models can predict the stock return series better than that of a random walk.
]]>Automation doi: 10.3390/automation4030013
Authors: Almira Budiyanto Nobutomo Matsunaga
Nowadays, academic research, disaster mitigation, industry, and transportation apply the cooperative multi-agent concept. A cooperative multi-agent system is a multi-agent system that works together to solve problems or maximise utility. The essential marks of formation control are how the multiple agents can reach the desired point while maintaining their position in the formation based on the dynamic conditions and environment. A cooperative multi-agent system closely relates to the formation change issue. It is necessary to change the arrangement of multiple agents according to the environmental conditions, such as when avoiding obstacles, applying different sizes and shapes of tracks, and moving different sizes and shapes of transport objects. Reinforcement learning is a good method to apply in a formation change environment. On the other hand, the complex formation control process requires a long learning time. This paper proposed using the Deep Dyna-Q algorithm to speed up the learning process while improving the formation achievement rate by tuning the parameters of the Deep Dyna-Q algorithm. Even though the Deep Dyna-Q algorithm has been used in many applications, it has not been applied in an actual experiment. The contribution of this paper is the application of the Deep Dyna-Q algorithm in formation control in both simulations and actual experiments. This study successfully implements the proposed method and investigates formation control in simulations and actual experiments. In the actual experiments, the Nexus robot with a robot operating system (ROS) was used. To confirm the communication between the PC and robots, camera processing, and motor controller, the velocities from the simulation were directly given to the robots. The simulations could give the same goal points as the actual experiments, so the simulation results approach the actual experimental results. The discount rate and learning rate values affected the formation change achievement rate, collision number among agents, and collisions between agents and transport objects. For learning rate comparison, DDQ (0.01) consistently outperformed DQN. DQN obtained the maximum −170 reward in about 130,000 episodes, while DDQ (0.01) could achieve this value in 58,000 episodes and achieved a maximum −160 reward. The application of an MEC (model error compensator) in the actual experiment successfully reduced the error movement of the robots so that the robots could produce the formation change appropriately.
]]>Automation doi: 10.3390/automation4030012
Authors: Sotiris Raptis Vasiliki Softa Georgios Angelidis Christos Ilioudis Kiki Theodorou
Radiomics has shown great promise in predicting various diseases. Researchers have previously attempted to include radiomics in their automated detection, diagnosis, and segmentation algorithms, taking these steps based on the promising outcomes of radiomics-based studies. As a result of the increased attention given to this topic, numerous institutions have developed their own radiomics software. These packages, on the other hand, have been utilized interchangeably without regard for their fundamental differences. The primary purpose of this study was to explore benefits of predictive model performance for radiation pneumonitis (RP), which is the most frequent side effect of chest radiotherapy, and through this work, we developed a radiomics model based on deep learning that intends to increase RP prediction performance by combining more data points and digging deeper into these data. In order to evaluate the most popular machine learning models, radiographic characteristics were used, and we recorded the most important of them. The high dimensionality of radiomic datasets is a major issue. The method proposed for use in data problems is the synthetic minority oversampling technique, which we used in order to create a balanced dataset by leveraging suitable hardware and open-source software. The present study assessed the efficacy of various machine learning models, including logistic regression (LR), support vector machine (SVM), random forest (RF), and deep neural network (DNN), in predicting radiation pneumonitis by utilizing specific radiomics features. The findings of the study indicate that the four models displayed satisfactory efficacy in forecasting radiation pneumonitis. The DNN model demonstrated the highest area under the receiver operating curve (AUC-ROC) value, which was 0.87, suggesting its superior predictive capacity among the models considered. The AUC-ROC values for the random forest, SVM, and logistic regression models were 0.85, 0.83, and 0.81, respectively.
]]>Automation doi: 10.3390/automation4020011
Authors: Umme Kawsar Alam Kassidy Shedd Mahdi Haghshenas-Jaryani
This paper presents a quasi-static model-based control algorithm for controlling the motion of a soft robotic exo-digit with three independent actuation joints physically interacting with the human finger. A quasi-static analytical model of physical interaction between the soft exo-digit and a human finger model was developed. Then, the model was presented as a nonlinear discrete-time multiple-input multiple-output (MIMO) state-space representation for the control system design. Input–output feedback linearization was utilized and a control input was designed to linearize the input–output, where the input is the actuation pressure of an individual soft actuator, and the output is the pose of the human fingertip. The asymptotic stability of the nonlinear discrete-time system for trajectory tracking control is discussed. A soft robotic exoskeleton digit (exo-digit) and a 3D-printed human-finger model integrated with IMU sensors were used for the experimental test setup. An Arduino-based electro-pneumatic control hardware was developed to control the actuation pressure of the soft exo-digit. The effectiveness of the controller was examined through simulation studies and experimental testing for following different pose trajectories corresponding to the human finger pose during the activities of daily living. The model-based controller was able to follow the desired trajectories with a very low average root-mean-square error of 2.27 mm in the x-direction, 2.75 mm in the y-direction, and 3.90 degrees in the orientation of the human finger distal link about the z-axis.
]]>Automation doi: 10.3390/automation4020010
Authors: Junya Sato
For a robot to pick up an object viewed by a camera, the object’s position in the image coordinate system must be converted to the robot coordinate system. Recently, a neural network-based method was proposed to achieve this task. This methodology can accurately convert the object’s position despite errors and disturbances that arise in a real-world environment, such as the deflection of a robot arm triggered by changes in the robot’s posture. However, this method has some drawbacks, such as the need for significant effort in model selection, hyperparameter tuning, and lack of stability and interpretability in the learning results. To address these issues, a method involving linear and nonlinear regressions is proposed. First, linear regression is employed to convert the object’s position from the image coordinate system to the robot base coordinate system. Next, B-splines-based nonlinear regression is applied to address the errors and disturbances that occur in a real-world environment. Since this approach is more stable and has better calibration performance with interpretability as opposed to the recent method, it is more practical. In the experiment, calibration results were incorporated into a robot, and its performance was evaluated quantitatively. The proposed method achieved a mean position error of 0.5 mm, while the neural network-based method achieved an error of 1.1 mm.
]]>Automation doi: 10.3390/automation4020009
Authors: Kaili Yang Yi Gan Yanlong Cao Jiangxin Yang Zijian Wu
Under the new geometric product specification (GPS), a two-dimensional chain cannot completely guarantee quality of the product. To optimize the allocation of three-dimensional tolerances in the conceptual design stage, the geometric variations of the tolerance zone to the deviation domain will be mapped in this paper. The deviation-processing cost, deviation-quality loss cost, and deviation-sensitivity cost function relationships combined with the tolerance zone described by the small displacement torsor theory are discussed. Then, synchronous constraint of the function structure and tolerance is realized. Finally, an improved bat algorithm is used to solve the established three-dimensional tolerance mathematical model. A case study in the optimization of three-part tolerance design is used to illustrate the proposed model and algorithms. The performance and advantage of the proposed method are discussed in the end.
]]>Automation doi: 10.3390/automation4010008
Authors: Vasiliki Balaska Eudokimos Theodoridis Ioannis-Tsampikos Papapetros Christoforos Tsompanoglou Loukas Bampis Antonios Gasteratos
The swift development of autonomous vehicles raises the necessity of semantically mapping the environment by producing distinguishable representations to recognise similar areas. To this end, in this article, we present an efficient technique to cut up a robot’s trajectory into semantically consistent communities based on graph-inspired descriptors. This allows an agent to localise itself in future tasks under different environmental circumstances in an urban area. The proposed semantic grouping technique utilizes the Leiden Community Detection Algorithm (LeCDA), which is a novel and efficient method of low computational complexity and exploits semantic and topometric information from the observed scenes. The presented experimentation was carried out on a novel dataset from the city of Xanthi, Greece (dubbed as Gryphonurban urban dataset), which was recorded by RGB-D, IMU and GNSS sensors mounted on a moving vehicle. Our results exhibit the formulation of a semantic map with visually coherent communities and the realisation of an effective localisation mechanism for autonomous vehicles in urban environments.
]]>Automation doi: 10.3390/automation4010007
Authors: Michel E. D. Chaves Anderson R. Soares Guilherme A. V. Mataveli Alber H. Sánchez Ieda D. Sanches
Land use and land cover (LULC) mapping initiatives are essential to support decision making related to the implementation of different policies. There is a need for timely and accurate LULC maps. However, building them is challenging. LULC changes affect natural areas and local biodiversity. When they cause landscape fragmentation, the mapping and monitoring of changes are affected. Due to this situation, improving the efforts for LULC mapping and monitoring in fragmented biomes and ecosystems is crucial, and the adequate separability of classes is a key factor in this process. We believe that combining multidimensional Earth observation (EO) data cubes and spectral vegetation indices (VIs) derived from the red edge, near-infrared, and shortwave infrared bands provided by the Sentinel-2/MultiSpectral Instrument (S2/MSI) mission reduces uncertainties in area estimation, leading toward more automated mappings. Here, we present a low-cost semi-automated classification scheme created to identify croplands, pasturelands, natural grasslands, and shrublands from EO data cubes and the Surface Reflectance to Vegetation Indexes (sr2vgi) tool to automate spectral index calculation, with both produced in the scope of the Brazil Data Cube (BDC) project. We used this combination of data and tools to improve LULC mapping in the Brazilian Cerrado biome during the 2018–2019 crop season. The overall accuracy (OA) of our results is 88%, indicating the potential of the proposed approach to provide timely and accurate LULC mapping from the detection of different vegetation patterns in time series.
]]>Automation doi: 10.3390/automation4010006
Authors: Isaí Vilches Félix Juárez Durán Alfonso Gómez-Espinosa Mary Carmen García Carrillo Jesús Arturo Escobedo Cabello
With the rise of Industry 4.0, its pillars (which include Internet of Things, “Big Data”, data analytics, augmented reality, cybersecurity, etc.) have become unavoidable tendencies for the automated manufacturing industry. Equipment upgrade is required to match the new standards of digitally assisted automation. However, not all factories in the medium to small range (or independent manufacturers) can afford to upgrade their equipment. Therefore, the availability of affordable Industry 4.0 upgrades for now-outdated devices is necessary for manufacturers in the SME range (Small-Medium Enterprises) to stay relevant and profitable. More specifically, this work revolves around the automation of printed circuit board (PCB) manufacturing, which is one of the most popular and profitable areas involved in this movement; and within it, the large majority of manufacturing defects can be traced to the soldering or “reflow” stage. Manufacturing research must, thus, aim towards improving reflow ovens and, more specifically, aim to improve their autonomous capabilities and affordability. This work presents the design and results of a controlling interface utilizing a Raspberry Pi 4 as a coupling interface between an MQTT Broker (which monitors the overall system) and the oven itself (which is, intentionally, a sub-prime model which lacks native IoT support), resulting in successful, remote, network-based controlling and monitoring of the oven. Additionally, it documents the design and implementation of the network adaptations necessary for it to be considered a cybersecure IIoT Module and connect safely to the Production Cell’s Subnet. All of this to address the inclusion of specific Industry 4.0 needs such as autonomous functioning, data collection and cybersecurity in outdated manufacturing devices and help enrich the processes of SME PCB manufacturers.
]]>Automation doi: 10.3390/automation4010005
Authors: Camilo Garcia-Tenorio Duvan Tellez-Castro Eduardo Mojica-Nava Alain Vande Wouwer
This paper provides the theoretical foundation for the approximation of the regions of attraction in hyperbolic and polynomial systems based on the eigenfunctions deduced from the data-driven approximation of the Koopman operator. In addition, it shows that the same method is suitable for analyzing higher-dimensional systems in which the state space dimension is greater than three. The approximation of the Koopman operator is based on extended dynamic mode decomposition, and the method relies solely on this approximation to find and analyze the system’s fixed points. In other words, knowledge of the model differential equations or their linearization is not necessary for this analysis. The reliability of this approach is demonstrated through two examples of dynamical systems, e.g., a population model in which the theoretical boundary is known, and a higher-dimensional chemical reaction system constituting an original result.
]]>Automation doi: 10.3390/automation4010004
Authors: Peter Lohmander
A proxy war between a coalition of countries, BLUE, and a country, RED, is considered. RED wants to increase the size of the RED territory. BLUE wants to involve more regions in trade and other types of cooperation. GREEN is a small and independent nation that wants to become a member of BLUE. RED attacks GREEN and tries to invade. BLUE decides to give optimal arms support to GREEN. This support can help GREEN in the war against RED and simultaneously can reduce the military power of RED, which is valuable to BLUE also outside this proxy war, since RED may confront BLUE also in other regions. The optimal control problem of dynamic arms support, from the BLUE perspective, is defined in general form. From the BLUE perspective, there is an optimal position of the front. This position is a function of the weights in the objective function and all other parameters. Optimal control theory is used to determine the optimal dynamic BLUE strategy, conditional on a RED strategy, which is observed by BLUE military intelligence. The optimal arms support strategy for BLUE is to initially send a large volume of arms support to GREEN, to rapidly move the front to the optimal position. Then, the support should be almost constant during most of the war, keeping the war front location stationary. In the final part of the conflict, when RED will have almost no military resources left and tries to retire from the GREEN territory, BLUE should strongly increase the arms support and make sure that GREEN rapidly can regain the complete territory and end the war.
]]>Automation doi: 10.3390/automation4010003
Authors: Automation Editorial Office Automation Editorial Office
High-quality academic publishing is built on rigorous peer review [...]
]]>Automation doi: 10.3390/automation4010002
Authors: Thommas Kevin Sales Flores Juan Moises Mauricio Villanueva Heber Pimentel Gomes
Automation and control systems are constantly evolving, using artificial intelligence techniques to implement new forms of control, such as fuzzy control, with advantages over classic control strategies, especially in non-linear systems. Water supply networks are complex systems with different operating configurations, uninterrupted operation requirements, equalization capacity and pressure control in the supply networks, and high reliability. In this sense, this work aims to develop a fuzzy pressure control system for a supply system with three possible operating configurations: a single motor pump, two motor pumps in series, or two motor pumps in parallel. For each configuration, an energy efficiency analysis was carried out according to the demand profile established in this case study. In order to validate the proposed methodology, an experimental water supply system was used, located in the Laboratory of Energy Efficiency and Hydraulics in Sanitation at the Federal University of Paraiba (LENHS/UFPB).
]]>Automation doi: 10.3390/automation4010001
Authors: Jana Pöpperlová Stephan Ottweiler Andreas Vossberg Ulrich Krupp
The aim of the presented feasibility study was to systematically investigate the automation of the skimming (i.e., removal) of zinc ash from the surface of the zinc bath in order to minimise the risks for workers due to mechanical hazards (risk of falling into the zinc kettle) and chemical hazards (inhalation exposure to zinc vapours) by eliminating this activity. As part of the feasibility study, automatic separation and skimming systems from various applications, such as removal systems of slags and metal foam, were identified. For this purpose, their technical feasibility and suitability were considered. Two automated techniques, a mechanical and a gas-based skimming system, were selected for the subsequent laboratory-based evaluation. In the scope of the practical feasibility study, the selected skimming techniques were designed, constructed, and evaluated based on near-process prototype tests on a laboratory scale. The focus was on the efficiency of the skimming systems, related to the removal of zinc ash from the free surface of the molten zinc (general efficiency), as well as to the zinc ash removal with a simulated attachment system of the samples to be galvanised (task-related efficiency). The desired complete removal of zinc ash from the zinc bath surface was demonstrated with two automated methods: a pulse wave method of the mechanical skimming system and a gas-based skimming system in general, operating independently from the attachment system. Additionally, as part of the process-related simulation of the complete batch galvanising process, a fully automated combination of the zinc ash skimming and extraction system was achieved on a laboratory scale.
]]>Automation doi: 10.3390/automation3040033
Authors: Marcel Nicola Claudiu-Ionel Nicola Dan Selișteanu Cosmin Ionete
Starting from the problem of studying the parametric robustness in the case of the control of a permanent magnet-synchronous motor (PMSM), although robust control systems correspond entirely to this problem, due to the complexity of the algorithms of the robust type, in this article the use of switched systems theory is proposed as a study option, given the fact that these types of systems are suitable both for the study of systems with variable structure and for systems with significant parametric variation under conditions of lower complexity of the control algorithms. The study begins by linearizing a PMSM model at a static operating point and continues with a systematic presentation of the basic elements and concepts concerning the stability of switched systems by applying these concepts to the control system of a PMSM based on the field-oriented control (FOC) strategy, which usually changes the value of its parameters during operation (stator resistance Rs, stator inductances Ld and Lq, but also combined inertia of PMSM rotor and load J). The numerical simulations performed in Simulink validate the fact that, for parametric variations of the PMSM structure, the PMSM control switched systems preserve qualitative performance in terms of its control. A series of Matlab programs are presented based on the YALMIP toolbox to obtain Pi matrices, by solving Lyapunov–Metzler type inequalities, and using dwell time to demonstrate stability, as well as the qualitative study of the performance of PMSM control switched systems by presenting in phase plane and state space analysis of the evolution of state vectors: ω PMSM rotor speed, iq current, and id current.
]]>Automation doi: 10.3390/automation3040032
Authors: Navreet S. Thind Justus Hering Dirk Söffker
Vessel motion simulation as well as model-based accurate trajectory prediction of vessels require accurate models with respect to related dynamic properties. The ability to predict vessel’s trajectory behaviors will become relevant in the case of future autonomous navigation of vessels to predict the behavior of others. The definition of models or parameters can be realized via first principles or by using experimental modeling methods leading to a time invariant or variant model. Existing hydrodynamical modeling approaches are based on mathematical approaches, which use parameters like mass, hydrodynamic forces, wind velocity, depth under the keel, loading parameters, etc. So, determining a dynamic vessel’s model is a complex task, since the model is vessel-specific. For collision avoidance of autonomous or assisted vessels, the trajectory prediction of encountering other vessels is especially required. It is not possible to use complex hydrodynamical models of encountering vessels online due to missing required information/measurements. Even existing deep learning approaches provide better predictions, but are still insufficient for collision avoidance in the case of strong dynamical changes, since the considered input sequences are long. Due to long input sequences, the model does not adapt to strong dynamical changes. In this work, a simple parameter-based approach is developed to predict the intended behavior using the last seconds of the measured position variables. The idea is to globally identify the model parameters of the vessel, which remains constant for the situation, and additionally two parameters for local adaptation, which adapt at every updated input sequence. Typically parameters like rudder angle, wind velocities, and water current affect the behavior of vessels. The introduced approach works with a sliding window approach for which, after identification of the global system, local values are identified based on the last 80 measurements of the vessels. A trajectory prediction (assuming no additional rudder-based maneuvering) is realized for the prediction horizon of 180 s. To confirm the robustness of the new approach, real AIS/GPS-based measurements from a German research inland vessel for different scenarios and sailing conditions including ‘loaded’ and ‘empty’ sailing cases are used. Furthermore, additional results are shown for position data information of different sample rates.
]]>Automation doi: 10.3390/automation3040031
Authors: Matheus K. Gomes Willian H. A. da Silva Antonio Ribas Neto Julio Fajardo Eric Rohmer Eric Fujiwara
Force myography (FMG) detects hand gestures based on muscular contractions, featuring as an alternative to surface electromyography. However, typical FMG systems rely on spatially-distributed arrays of force-sensing resistors to resolve ambiguities. The aim of this proof-of-concept study is to develop a method for identifying hand poses from the static and dynamic components of FMG waveforms based on a compact, single-channel optical fiber sensor. As the user performs a gesture, a micro-bending transducer positioned on the belly of the forearm muscles registers the dynamic optical signals resulting from the exerted forces. A Raspberry Pi 3 minicomputer performs data acquisition and processing. Then, convolutional neural networks correlate the FMG waveforms with the target postures, yielding a classification accuracy of (93.98 ± 1.54)% for eight postures, based on the interrogation of a single fiber transducer.
]]>Automation doi: 10.3390/automation3040030
Authors: Wael Alsabbagh Peter Langendörfer
Programmable logic controllers (PLCs) make up a substantial part of critical infrastructures (CIs) and industrial control systems (ICSs). They are programmed with a control logic that defines how to drive and operate critical processes such as nuclear power plants, petrochemical factories, water treatment systems, and other facilities. Unfortunately, these devices are not fully secure and are prone to malicious threats, especially those exploiting vulnerabilities in the control logic of PLCs. Such threats are known as control logic injection attacks. They mainly aim at sabotaging physical processes controlled by exposed PLCs, causing catastrophic damage to target systems as shown by Stuxnet. Looking back over the last decade, many research endeavors exploring and discussing these threats have been published. In this article, we present a flashback on the recent works related to control logic injection attacks against PLCs. To this end, we provide the security research community with a new systematization based on the attacker techniques under three main attack scenarios. For each study presented in this work, we overview the attack strategies, tools, security goals, infected devices, and underlying vulnerabilities. Based on our analysis, we highlight the current security challenges in protecting PLCs from such severe attacks and suggest security recommendations for future research directions.
]]>Automation doi: 10.3390/automation3040029
Authors: Abdel-Nasser Sharkawy Mahmoud Hasanin Mohamed Sharf Mahmoud Mohamed Ahmed Elsheikh
The ideal smart home could be automatically controlled using a variety of electronic tools and devices to perform everyday tasks. Smart home automation is crucially beneficial for human life, particularly when considering those with disabilities, inpatients, and elderly populations. In this paper, applications and systems for smart homes are investigated. During experimentation they were controlled via an Android mobile phone and the Arduino platform. Bluetooth Module HC-06 was used to connect the Arduino Uno R3 with the mobile phone. Five smart home applications were developed to control the lighting and electrical sockets, fan speed, temperature- and humidity-meter display/controls, as well as the fire-alarm and toxic-gas alarm systems. Herein, the definition, the graphical user interface, the required main components, and the control circuit connections are prepared and presented for each application. The graphical user interface was created using the RemoteXY website, which is a reliable website for this purpose. The developed applications were tested, and they were found to work efficiently and correctly. Additionally, this innovative system is both cost-effective and affordable (total cost at the time of development was 110 USD).
]]>Automation doi: 10.3390/automation3040028
Authors: Lijuan Long Yonghua Xia Minglong Yang Bin Wang Yirong Pan
When constructing a three-dimensional model of a transformer substation, it is critical to quickly find the 3D CAD model corresponding to the current point cloud data from a large number of transformer substation model libraries (due to the complexity and variety of models in the model base). In response to this problem, this paper proposes a method to quickly retrieve a 3D CAD model. Firstly, a 3D CAD model that shares the same size as the current point cloud model bounding box is extracted from the model library by the double-layer bounding box screening method. Then, the selected 3D CAD model is finely compared with the point cloud model by the multi-view method. The 3D CAD model that has the highest degree of corresponding to the point cloud data is acquired. The proposed algorithm, compared to other similar methods, has the advantages of retrieval accuracy and high efficiency.
]]>Automation doi: 10.3390/automation3040027
Authors: Haruhiro Shiraishi Hajime Shiraishi
Trawling is one of the most common fishing methods used by small vessels. This method requires the vessel to operate at a constant low speed because the depth of the trawl must be kept constant. In addition, the operation is often conducted by a small number of people, who must simultaneously maneuver the vessel and fish, making automation desirable. To develop this device, a mathematical model of the vessel was created based on data collected from actual operation of the vessel, and simulations were conducted to determine what type of control system would be suitable. As a result, it was possible to grasp effective control methods, effects of disturbances such as tides and waves, and how to deal with effective parts to improve response.
]]>Automation doi: 10.3390/automation3030026
Authors: Boris Andrievsky Alexander M. Popov Ilya Kostin Julia Fadeeva
This survey deals with the problem of the group motion of spacecraft, which is rapidly developing and relevant for many applications, in terms of developing the onboard control algorithms to ensure the fulfillment of a given mission. The paper provides a comprehensive overview of spacecraft formation flight control. The bibliography is divided into three main sections: the multiple-input–multiple-output approach, in which the formation is treated as a single entity with multiple inputs and multiple outputs; the leader–follower formation, in which individual spacecraft controllers are linked hierarchically; and a virtual structure formation, in which spacecraft are treated as rigid bodies embedded in a common virtual rigid body. This survey expands a 2004 survey and updates it with recent results.
]]>Automation doi: 10.3390/automation3030025
Authors: Nadjim Horri Mikolaj Pietraszko
Flight testing in a realistic three-dimensional virtual environment is increasingly being considered a safe and cost-effective way of evaluating aircraft models and their control systems. The paper starts by reviewing and comparing the most popular personal computer-based flight simulators that have been successfully interfaced to date with the MathWorks software. This co-simulation approach allows combining the strengths of Matlab toolboxes for functions including navigation, control, and sensor modeling with the advanced simulation and scene rendering capabilities of dedicated flight simulation software. This approach can then be used to validate aircraft models, control algorithms, flight handling chatacteristics, or perform model identification from flight data. There is, however, a lack of sufficiently detailed step-by-step flight co-simulation tutorials, and there have also been few attempts to evaluate more than one flight co-simulation approach at a time. We, therefore, demonstrate our own step-by-step co-simulation implementations using Simulink with three different flight simulators: Xplane, FlightGear, and Alphalink’s virtual flight test environment (VFTE). All three co-simulations employ a real-time user datagram protocol (UDP) for data communication, and each approach has advantages depending on the aircraft type. In the case of a Cessna-172 general aviation aircraft, a Simulink co-simulation with Xplane demonstrates successful virtual flight tests with accurate simultaneous tracking of altitude and speed reference changes while maintaining roll stability under arbitrary wind conditions that present challenges in the single propeller Cessna. For a medium endurance Rascal-110 unmanned aerial vehicle (UAV), Simulink is interfaced with FlightGear and with QGroundControl using the MAVlink protocol, which allows to accurately follow the lateral UAV path on a map, and this setup is used to evaluate the validity of Matlab-based six degrees of freedom UAV models. For a smaller ZOHD Nano Talon miniature aerial vehicle (MAV), Simulink is interfaced with the VFTE, which was specifically designed for this MAV, and with QGroundControl for the testing of advanced H-infinity observer-based autopilots using a software-in-the-loop (SIL) simulation to achieve robust low altitude flight under windy conditions. This is then finally extended to hardware-in-the-loop (HIL) implementation on the Nano Talon MAV using a controller area network (CAN) databus and a Pixhawk-4 mini autopilot with simulated sensor models.
]]>Automation doi: 10.3390/automation3030024
Authors: Rens Baeyens Joachim Denil Jan Steckel Walter Daems
High-performance sensing and control systems have an important role in Industry 4.0. However, with the current solutions, the development effort is high and requires specialized skills in electronic engineering. Therefore, a model-based approach on control and signal processing systems using affordable heterogeneous hardware is proposed. In this work, a model-based code generator is developed to abstract the user from the actual software implementation. Starting from a combined model of a timing diagram and an embedded platform, a model transformation is used to automatically generate functional acquisition firmware. This firmware generator enables system engineers without deep software and hardware knowledge to set up complex control systems. Furthermore, it equips software engineers with a solid framework for faster development.
]]>Automation doi: 10.3390/automation3030023
Authors: Emanuel Martinez Villanueva Jennifer Alejandra Cardenas Castañeda Rafiq Ahmad
Cross-laminated timber (CLT) has been one of the principal materials in mass timber construction, and now it is possible to find mid-rise and high-rise projects around the globe. This study makes a scientometric review comparison between CLT and the impact of the fourth industrial revolution (formally known as Industry 4.0) in the construction industry, focusing on worldwide academic publications between 2006 and 2022. The analysis considers keywords, co-author, co-citation, and clustering analysis. This study used 1320 documents, including journals and conference proceedings from the Scopus database, where 753 were for cross-laminated timber and 567 for Industry 4.0. Key researchers, research institutions, journals, publications, citation patterns, and trends are some of the results obtained from the scientometric analysis. Once the knowledge mapping was conducted for both fields, scrutiny of the interconnection of both areas was performed to find possible research gaps from a manufacturing perspective. Among the conclusions, it is logical to say that Industry 4.0 implementation in cross-laminated timber is still in its infancy. One of the most popular technologies impacting construction is the digital twin concept; however, no work is reported for CLT on this topic. Additionally, digital automation is a necessity in any research practice, and the use of industrial robots is shown to be an essential asset for CLT as these robots can handle complex shapes.
]]>Automation doi: 10.3390/automation3030022
Authors: Robert Ross Ben Anderson Brian Bienvenu Emily L. Scicluna Kylie A. Robert
Wildlife tracking is used to acquire information on the movement, behaviour and survival of animals in their natural habitat for a wide range of ecological questions. However, tracking and monitoring free-ranging animals in the field is typically labour-intensive and particularly difficult in species that are small, cryptic, or hard to re-capture. In this paper, we describe and evaluate an Internet-of-Things (IoT)-based tracking system which automatically logs detected passive RFID tags and uploads them to the cloud. This system was successfully evaluated with 90 sensor modules deployed in a 30 ha wildlife sanctuary to monitor a small nocturnal mammal of less than 20 g in body size.
]]>Automation doi: 10.3390/automation3030021
Authors: M. Azizur Rahman Md Shihab Shakur Md. Sharjil Ahamed Shazid Hasan Asif Adnan Rashid Md Ariful Islam Md. Sabit Shahriar Haque Afzaal Ahmed
With the advancement of additive manufacturing (AM), or 3D printing technology, manufacturing industries are driving towards Industry 4.0 for dynamic changed in customer experience, data-driven smart systems, and optimized production processes. This has pushed substantial innovation in cyber-physical systems (CPS) through the integration of sensors, Internet-of-things (IoT), cloud computing, and data analytics leading to the process of digitization. However, computer-aided design (CAD) is used to generate G codes for different process parameters to input to the 3D printer. To automate the whole process, in this study, a customer-driven CPS framework is developed to utilize customer requirement data directly from the website. A cloud platform, Microsoft Azure, is used to send that data to the fused diffusion modelling (FDM)-based 3D printer for the automatic printing process. A machine learning algorithm, the multi-layer perceptron (MLP) neural network model, has been utilized for optimizing the process parameters in the cloud. For cloud-to-machine interaction, a Raspberry Pi is used to get access from the Azure IoT hub and machine learning studio, where the generated algorithm is automatically evaluated and determines the most suitable value. Moreover, the CPS system is used to improve product quality through the synchronization of CAD model inputs from the cloud platform. Therefore, the customer’s desired product will be available with minimum waste, less human monitoring, and less human interaction. The system contributes to the insight of developing a cloud-based digitized, automatic, remote system merging Industry 4.0 technologies to bring flexibility, agility, and automation to AM processes.
]]>Automation doi: 10.3390/automation3030020
Authors: Thomas Usländer Michael Baumann Stefan Boschert Roland Rosen Olaf Sauer Ljiljana Stojanovic Jan Christoph Wehrstedt
This paper proposes to combine the concept of digital twins with the concept of dataspaces to fulfill the original expectation that a digital twin is a comprehensive virtual representation of physical assets. Based upon a terminological and conceptual discussion of digital twins and dataspaces, this paper claims that a systemic approach towards digital twin Systems is required. The key conceptual approach consists of a Reference Model for Digital Twin Systems (DTS-RM) and a hypothesis regarding a symbiotic evolution. The DTS-RM distinguishes between a digital twin back-end platform comprising the access and management of comprehensive digital twin instances and digital twin-related services, and digital twin front-end services that are tailored to the demands of applications and users. The main purpose of the back-end platform is to decouple the digital twin’s generation and management from the usage of the digital twin for applications.
]]>Automation doi: 10.3390/automation3030019
Authors: Kazuki Nirayama Shoichiro Takehara Satoshi Takayama Yusuke Ito
Tethers (strings and wires) are used in various mechanical systems because they are lightweight and have excellent storability. Examples of such systems include elevators and cranes. In recent years, the use of tethers in special environments, such as outer space, is expected, and various systems have been proposed. In this study, we propose a mobility system using a tether that moves a human by winding a tether attached to a wall. However, the method has a problem whereby the attitude of the human can lack stability during the winding of the tether. We developed the attitude control method of the Tether Space Mobility Device during tether winding while focusing on fluctuations in the rotational kinetic energy of systems. The effectiveness of the control method was shown using numerical simulation. In this paper, the proposed control system is installed in the experimental device for validating the numerical simulation model. Then, we verified the effectiveness of the proposed control method through experiments using an actual system. The experimental results confirm that the angular velocity of the Tether Space Mobility Device converges to 0 deg/s when control is applied. In addition, it was shown that the proposed control method is effective for automatically winding the tether.
]]>Automation doi: 10.3390/automation3030018
Authors: Mateusz Malarczyk Mateusz Zychlewicz Radoslaw Stanislawski Marcin Kaminski
The paper is focused on issues related to the control of electrical drives with oscillations of state variables. The main problem deals with the construction of the mechanical part, which contains elastic elements used as a coupling between the motor machine and the load. In such cases, strict tracking of the reference trajectory is difficult, so damping of the disturbances is necessary. For this purpose, the full state vector of the object is applied as the feedback signal for the speed controller. This method is efficient and relatively easy to implement (including the hardware part). However, the control accuracy is dependent on the quality of the parameters identification and the invariance of the object. Thus, two adaptive structures are proposed for the two-mass system. Moreover, selected coefficients were optimized using metaheuristic algorithms (symbiotic organism search and flower pollination algorithm). After presentation of the preliminaries and mathematical background, tests were conducted, and the numerical simulations are shown. Finally, the experimental verification for the 0.5 kW DC machines was performed. The results confirm the theoretical concept and the initial assumptions: the state controller leads to the precise control of the drive with a long shaft; recalculation of the parameters can improve the work of the drive under changes of time constants; modern design tools are appropriate for this application.
]]>Automation doi: 10.3390/automation3030017
Authors: Georgios Pappas Joshua E. Siegel Eva Kassens-Noor Jacob Rutkowski Konstantinos Politopoulos Antonis A. Zorpas
We identify the need for enhanced pedestrian–vehicle simulation tools and build such a tool to explore the interaction among pedestrian “players” and virtual human- and automated-vehicles for different scenarios taking place in an urban environment. We first present contemporary research tools and then propose the design and development of a new desktop application that facilitates pedestrian-point-of-view research. We then conduct a three-step user experience experiment, in which a small number of participants answer questions before and after using the application to interact with virtual human and automated vehicles in diverse road-crossing scenarios. Behavioral results observed in virtuality, especially when motivated by consequence, tend to simulate real life sufficiently well to inform design choices. From the simulation, we observed valuable insights into human–vehicle interactions. Upon completing this preliminary testing, we iterated the tool’s design and ultimately conducted an 89-participant study of human–vehicle interactions for three scenarios taking place in a virtual environment. Our tool raised participant awareness of autonomous vehicles and their capabilities and limitations, which is an important step in overcoming public distrust of AVs. We additionally saw that participants trust humans and technology less as drivers than in other contexts, and that pedestrians feel safer around vehicles with autonomy indicators. Further, we note that study participants increasingly feel safe with automated vehicles with increased exposure. These preliminary results, as well as the efficacy of the tool’s design, may inform future socio-technical design for automated vehicles and their human interactions.
]]>Automation doi: 10.3390/automation3020016
Authors: Louis A. Catalano Zhiyong Hu Hakki Erhan Sevil
This paper outlines the methods, results, and statistical analysis of a model we developed to demonstrate the feasibility of applying remote sensor meteorological data to navigation by using meteorological contour matching (METCOM). Terrain contour matching (TERCOM), a contemporary navigation system, possesses inherent performance flaws that may be resolved and improved by METCOM for subsonic and hypersonic missile or aircraft navigation. Remote sensor imagery data for this model was accessed from the Geostationary Operational Environmental Satellites-R Series operated by the National Oceanic and Atmospheric Administration by using Amazon Web Services through a script we developed in Python. Data processed for the model included imagery data and corresponding geospatial data from the legacy atmospheric profile products: legacy vertical temperature and legacy vertical moisture. Our analysis of the model included an error assessment to determine model accuracy, geostatistical analysis through semivariograms, meteorological signal of model data, and a combinatorial analysis to evaluate navigation performance. We conducted a model assessment which indicated an accuracy of 66.2% in the data used as a combined result of instrument error and interference of cloud formations. Results of the remaining analysis offered methods to evaluate METCOM performance and compare different meteorological data products. These results allowed us to statistically compare METCOM and TERCOM, yielding several indications of improved performance including an increase by a factor of at least 13.5 in data variability and contourability. The analysis we conducted served as a proof of concept to justify further research into the feasibility and application of METCOM.
]]>Automation doi: 10.3390/automation3020015
Authors: Cédric Join Alberto d’Onofrio Michel Fliess
A continuously time-varying transmission rate is suggested by many control-theoretic investigations on non-pharmaceutical interventions for mitigating the COVID-19 pandemic. However, such a continuously varying rate is impossible to implement in any human society. Here, we significantly extend a preliminary work (M. Fliess, C. Join, A. d’Onofrio, Feedback control of social distancing for COVID-19 via elementary formulae, MATHMOD, Vienna, 2022), based on the combination of flatness-based and model-free controls with respect to the classic parsimonious SIR model. Indeed, to take into account severe uncertainties and perturbations, we propose a feedback control where the transmission rate, i.e., the control variable, is piecewise constant. More precisely, the transmission rate remains constant during an appreciable time interval, which is not too large. Strict extended lockdowns may therefore be avoided. The poor knowledge of fundamental quantities such as the rate of infection hinders a precise calibration of the transmission rate. Thus, the results of our approach ought therefore not to be regarded as rules of action to follow accurately but as a guideline for a wise behaviour.
]]>Automation doi: 10.3390/automation3020014
Authors: Weichao He Jiayan Wen Jin Tao Qinglin Sun
In order to achieve an accurate airdrop in the actual environment, the influence of complex interferences, such as wind field and the terrain of the environment, must be taken into account. Aiming at this problem, a combined trajectory planning strategy of a parafoil system subjected to intricate conditions is proposed in this paper. This method divides the parafoil airdrop area into an obstacle area and a landing area, then, considering the terrain environment surface, a model for the parafoil system in the wind field is built in the obstacle area. The Gauss pseudo-spectral method is used to transform the complex terrain environment constraint into a series of nonlinear optimal control problems with complex constraints. Finally, the trajectory of the landing area is designed by means of multiphase homing, and the target parameters are solved by the improved marine predator algorithm. The simulation results show that the proposed method has better realizability than a single homing strategy, and the optimization results of the improved marine predator algorithm have higher accuracy.
]]>Automation doi: 10.3390/automation3020013
Authors: Paulo Magalhaes Nuno Ferreira
In this study, we analyze the potential of collaborative robotics in automated quality inspections in the industry. The development of a solution integrating an industrial vision system allowed evaluating the performance of collaborative robots in a real case. The use of these tools allows reducing quality defects as well as costs in the manufacturing process. In this system, image processing methods use resources based on depth and surface measurements with high precision. The system fully processes a panel, observing the state of the surface to detect any potential defect in the panels produced to increase the quality of production.
]]>Automation doi: 10.3390/automation3020012
Authors: Alessandro Sapienza Filippo Cantucci Rino Falcone
Trust has been clearly identified as a key concept for human–machine interaction (HMI): on the one hand, users should trust artificial systems; on the other hand, devices must be able to estimate both how much other agents trust them and how trustworthy the other agents are. Indeed, the applications of trust in these scenarios are so complex that often, the interaction models consider only a part of the possible interactions and not the system in its entirety. On the contrary, in this work, we made the effort to consider the different types of interaction together, showing the advantages of this approach and the problems it allows to face. After the theoretical formalization, we introduce an agent simulation to show the functioning of the proposed model. The results of this work provide interesting insights for the evolution of HMI models.
]]>Automation doi: 10.3390/automation3010011
Authors: Natanael Magno Gomes Felipe Nascimento Martins José Lima Heinrich Wörtche
The number of applications in which industrial robots share their working environment with people is increasing. Robots appropriate for such applications are equipped with safety systems according to ISO/TS 15066:2016 and are often referred to as collaborative robots (cobots). Due to the nature of human-robot collaboration, the working environment of cobots is subjected to unforeseeable modifications caused by people. Vision systems are often used to increase the adaptability of cobots, but they usually require knowledge of the objects to be manipulated. The application of machine learning techniques can increase the flexibility by enabling the control system of a cobot to continuously learn and adapt to unexpected changes in the working environment. In this paper we address this issue by investigating the use of Reinforcement Learning (RL) to control a cobot to perform pick-and-place tasks. We present the implementation of a control system that can adapt to changes in position and enables a cobot to grasp objects which were not part of the training. Our proposed system uses deep Q-learning to process color and depth images and generates an ϵ-greedy policy to define robot actions. The Q-values are estimated using Convolution Neural Networks (CNNs) based on pre-trained models for feature extraction. To reduce training time, we implement a simulation environment to first train the RL agent, then we apply the resulting system on a real cobot. System performance is compared when using the pre-trained CNN models ResNext, DenseNet, MobileNet, and MNASNet. Simulation and experimental results validate the proposed approach and show that our system reaches a grasping success rate of 89.9% when manipulating a never-seen object operating with the pre-trained CNN model MobileNet.
]]>Automation doi: 10.3390/automation3010010
Authors: César Martínez-Olvera
Digital Twins (DTs) are one of the disruptive technologies associated with the Industry 4.0 concept. A DT connects the physical manufacturing system with the digital cyberspace, via the synchronization of the simulation (i.e., physical configurations) and data models (i.e., product, process, and resource models) of the manufacturing system. This synchronization of both worlds—the physical and digital—allows one to address the issue of manufacturing customized products. This challenge of mass customization (1) puts forward the goal of achieving the highest level of customer satisfaction, and (2) creates the need for the optimization of the complete value creation process. Within an Industry 4.0 context, the latter is translated as the interlinking of production resources and systems, via a DT, as it is in the physical world where the actual value-creation process takes place. The success of an Industry 4.0 mass customization environment (or mass customization 4.0), depends on its degree/level of sustainability. For these reasons, the present paper presents a review of relevant concepts related to the role of DTs in the achievement of a mass customization 4.0 environment, plus some proposals of how to address the identified research challenges. A future research agenda is proposed at the end of the paper.
]]>Automation doi: 10.3390/automation3010009
Authors: Cristian Berceanu Monica Pătrașcu
Complex networks make an enticing research topic that has been increasingly attracting researchers from control systems and various other domains over the last two decades. The aim of this paper was to survey the interest in control related to complex networks research over time since 2000 and to identify recent trends that may generate new research directions. The survey was performed for Web of Science, Scopus, and IEEEXplore publications related to complex networks. Based on our findings, we raised several questions and highlighted ongoing interests in the control of complex networks.
]]>Automation doi: 10.3390/automation3010008
Authors: Tiago Coito Paulo Faria Miguel S. E. Martins Bernardo Firme Susana M. Vieira João Figueiredo João M. C. Sousa
In the last few decades, there has been a growing necessity for systems that handle market changes and personalized customer needs with near mass production efficiency, defined as the new mass customization paradigm. The Industry 5.0 vision further enhances the human-centricity aspect, in the necessity for manufacturing systems to cooperate with workers, taking advantage of their problem-solving capabilities, creativity, and expertise of the manufacturing process. A solution is to develop a flexible manufacturing system capable of handling different customer requests and real-time decisions from operators. This paper tackles these aspects by proposing a digital twin of a robotic system for solution preparation capable of making real-time scheduling decisions and forecasts using a simulation model while allowing human interventions. A discrete event simulation model was used to forecast possible system improvements. The simulation handles real-time scheduling considering the possibility of adding identical parallel machines. Results show that processing multiple jobs simultaneously with more than one machine on critical processes, increasing the robot speed, and using heuristics that emphasize the shortest transportation time can reduce the overall completion time by 82%. The simulation model has an animated visualization window for a deeper understanding of the system.
]]>Automation doi: 10.3390/automation3010007
Authors: Parinaz Balkhi Mehrdad Moallem
In recent years, there has been growing interest in automated tracking and detection of sports activities. Researchers have shown that providing activity information to individuals during their exercise routines can greatly help them in achieving their exercise goals. In particular, such information would help them to maximize workout efficiency and prevent overreaching and overtraining. This paper presents the development of a novel multipurpose wearable device for automatic weight detection, activity type recognition, and count repetition in sports activities such as weight training. The device monitors weights and activities by using an inertial measurement unit (IMU), an accelerometer, and three force sensors mounted in a glove, and classifies them by utilizing developed machine learning models. For weight detection purposes, different classifiers including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Multi-layer Perceptron Neural Networks (MLP) were investigated. For activity recognition, the K nearest neighbor (KNN), Decision Tree (DT), Random Forest (RF), and SVM models were trained and examined. Experimental results indicate that the SVM classifier can achieve the highest accuracy for weight detection whereas RF can outperform other classifiers for activity recognition. The results indicate feasibility of developing a wearable device that can provide in-situ accurate information regarding the lifted weight and activity type with minimum physical intervention.
]]>Automation doi: 10.3390/automation3010006
Authors: Mateus Tonini Eitelwein Tiago Rodrigues Tavares José Paulo Molin Rodrigo Gonçalves Trevisan Rafael Vieira de Sousa José Alexandre Melo Demattê
Mapping soil fertility attributes at fine spatial resolution is crucial for site-specific management in precision agriculture. This paper evaluated the performance of mobile measurements using visible and near-infrared spectroscopy (vis–NIR) to predict and map key fertility attributes in tropical soils through local data modeling with partial least squares regression (PLS) and artificial neural network (ANN). Models were calibrated and tested in a calibration area (18-ha) with high spatial variability of soil attributes and then extrapolated in the entire field (138-ha). The models calibrated with ANN obtained superior performance for all attributes. Although ANN models obtained satisfactory predictions in the calibration area (ratio of performance to interquartile range (RPIQ) ≥ 1.7) for clay, organic matter (OM), cation exchange capacity (CEC), base saturation (V), and exchangeable (ex-) Ca, it was not repeated for some of them when extrapolated into the entire field. In conclusion, robust mappings (RPIQ = 2.49) were obtained for clay and OM, indicating that these attributes can be successfully mapped on tropical soils using mobile vis–NIR spectroscopy and local calibrations using ANN. This study highlights the need to implement an independent test to assess reliability and extrapolability of previously calibrated models, even when extrapolating the models to neighboring areas.
]]>Automation doi: 10.3390/automation3010005
Authors: Viorel Mînzu Iulian Arama
The closed-loop optimal control systems using the receding horizon control (RHC) structure make predictions based on a process model (PM) to calculate the current control output. In many applications, the optimal prediction over the current prediction horizon is calculated using a metaheuristic algorithm, such as an evolutionary algorithm (EA). The EAs, as other population-based metaheuristics, have large computational complexity. When integrated into the controller, the EA is carried out at each sampling moment and subjected to a time constraint: the execution time should be smaller than the sampling period. This paper proposes a software module integrated into the controller, called at each sampling moment. The module estimates using the PM integration the future process states, over a short time horizon, for different control input values covering the given technological interval. Only a narrower interval is selected for a ‘good’ evolution of the process, based on the so-called ‘state quality criterion’. The controller will consider only a shrunk control output range for the current sampling period. EA will search for its best prediction inside a smaller domain that does not cause the convergence to be affected. Simulations prove that the computational complexity of the controller will decrease.
]]>Automation doi: 10.3390/automation3010004
Authors: Zeinab Salehi Paknoosh Karimaghaee Shabnam Salehi Mohammad-Hassan Khooban
This paper presents a new passivity-preserving order reduction method for linear time-invariant passive systems, which are also called positive real (PR) systems, with the aid of the balanced truncation (BT) method. The proposed method stems from the conic positive real balanced truncation (CPRBT) method, which is a modification of the BT method for PR systems. CPRBT presents an algorithm in which the reduced models are obtained from some Riccati equations in which the phase angle of the transfer function has been taken into consideration. Although CPRBT is a powerful algorithm for obtaining accurate PR reduced-order models, it cannot guarantee that the phase diagram of the reduced model remains inside the same interval as that of the original full-order system. We aim to address such a problem by modifying CPRBT in the way that the phase angle of the reduced transfer function always remains inside the conic and homolographic phase interval of the original system. This is proven through some matrix manipulations, which has added mathematical value to the paper. Finally, in order to assess the efficacy of the proposed method, two numerical examples are simulated.
]]>Automation doi: 10.3390/automation3010003
Authors: Marcel Nicola
This article presents the study of the stability of single-input and multiple-input systems with point or distributed state delay and input delay and input saturation. By a linear transformation applied to the initial system with delay, a system is obtained without delay, but which is equivalent from the point of view of stability. Next, using sufficient conditions for the global asymptotic stability of linear systems with bounded control, new sufficient conditions are obtained for global asymptotic stability of the initial system with state delay and input delay and input saturation. In addition, the article presents the results on the instability and estimation of the stability region of the delay and input saturation system. The numerical simulations confirming the results obtained on stability were performed in the MATLAB/Simulink environment. A method is also presented for solving a transcendental matrix equation that results from the process of equating the stability of the systems with and without delay, a method which is based on the computational intelligence, namely, the Particle Swarm Optimization (PSO) method.
]]>Automation doi: 10.3390/automation3010002
Authors: Joga Dharma Setiawan Muhammad Aldi Septiawan Mochammad Ariyanto Wahyu Caesarendra M. Munadi Sabri Alimi Maciej Sulowicz
Indonesia is a maritime country that has vast coastal resources and biodiversity. To support the Indonesian maritime program, a topography mapping tool is needed. The ideal topography mapping tool is the Unmanned Surface Vehicle (USV). This paper proposes the design, manufacture, and development of an affordable autonomous USV. The USV which is composed of thruster and rudder is quite complicated to build. This study employs rudderless and double thrusters as the main actuators. PID compensator is utilized as the feedback control for the autonomous USV. Energy consumption is measured when the USV is in autonomous mode. The Dynamics model of USV was implemented to study the roll stability of the proposed USV. Open-source Mission Planner software was selected as the Ground Control Station (GCS) software. Performance tests were carried out by providing the USV with an autonomous mission to follow a specific trajectory. The results showed that the developed USV was able to complete autonomous mission with relatively small errors, making it suitable for underwater topography mapping.
]]>Automation doi: 10.3390/automation3010001
Authors: Marcela Lopez Mahdi Haghshenas-Jaryani
This paper presents the concept of muscle-driven locomotion for planar snake robots, which combines the advantages of both rigid and soft robotic approaches to enhance the performance of snake robot locomotion. For this purpose, two adjacent links are connected by a pair of pneumatic artificial muscles wherein an alternate actuation of these soft actuators causes a rotational motion at the connecting joints. The muscle-based actuated linkage mechanism, as a closed six-linkage mechanism, was designed and prototyped. The linear motion and force generation of the pneumatic artificial muscle was experimentally characterized using isotonic and isometric contraction experiments. A predictive model was developed based on the experimental data to describe the relationship between the force–length–pressure of the PAMs. Additionally, the muscle-driven mechanism was kinematically and dynamically characterized based on both theoretical and experimental studies. The experimental data generally agreed with our model’s results and the generated joint angle and torque were comparable to the current snake-like robots. A skx-link planar snake robot with five joints, five pairs of antagonistic muscles, and an associated pneumatic controller was prototyped and examined for simple movements on a straight-line. We demonstrated the muscle-driven locomotion of the six-link snake robot, and the results show the feasibility of using the proposed mechanism for future explorations of snake robot locomotion.
]]>Automation doi: 10.3390/automation2040017
Authors: Romain Delpoux Thierry Floquet Hebertt Sira-Ramírez
In this paper, an algebraic approach for the finite-time feedback control problem is provided for second-order systems where only the second-order derivative of the controlled variable is measured. In practice, it means that the acceleration is the only variable that can be used for feedback purposes. This problem appears in many mechanical systems such as positioning systems and force-position controllers in robotic systems and aerospace applications. Based on an algebraic approach, an on-line algebraic estimator is developed in order to estimate in finite time the unmeasured position and velocity variables. The obtained expressions depend solely on iterated integrals of the measured acceleration output and of the control input. The approach is shown to be robust to noisy measurements and it has the advantage to provide on-line finite-time (or non-asymptotic) state estimations. Based on these estimations, a quasi-homogeneous second-order sliding mode tracking control law including estimated position error integrals is designed illustrating the possibilities of finite-time acceleration feedback via algebraic state estimation.
]]>Automation doi: 10.3390/automation2040016
Authors: Alfonso Gómez-Espinosa Jesús B. Rodríguez-Suárez Enrique Cuan-Urquizo Jesús Arturo Escobedo Cabello Rick L. Swenson
The necessity for intelligent welding robots that meet the demand in real industrial production, according to the objectives of Industry 4.0, has been supported owing to the rapid development of computer vision and the use of new technologies. To improve the efficiency in weld location for industrial robots, this work focuses on trajectory extraction based on color features identification on three-dimensional surfaces acquired with a depth-RGB sensor. The system is planned to be used with a low-cost Intel RealSense D435 sensor for the reconstruction of 3D models based on stereo vision and the built-in color sensor to quickly identify the objective trajectory, since the parts to be welded are previously marked with different colors, indicating the locations of the welding trajectories to be followed. This work focuses on 3D color segmentation with which the points of the target trajectory are segmented by color thresholds in HSV color space and a spline cubic interpolation algorithm is implemented to obtain a smooth trajectory. Experimental results have shown that the RMSE error for V-type butt joint path extraction was under 1.1 mm and below 0.6 mm for a straight butt joint; in addition, the system seems to be suitable for welding beads of various shapes.
]]>Automation doi: 10.3390/automation2040015
Authors: George Nantzios Nikolaos Baras Minas Dasygenis
It is evident that the technological growth of the last few decades has signaled the development of several application domains. One application domain that has expanded massively in recent years is robotics. The usage and spread of robotic systems in commercial and non-commercial environments resulted in increased productivity, efficiency, and higher quality of life. Many researchers have developed systems that improve many aspects of people’s lives, based on robotics. Most of the engineers use high-cost robotic arms, which are usually out of the reach of typical consumers. We fill this gap by presenting a low-cost and high-accuracy project to be used as a robotic assistant for every consumer. Our project aims to further improve people’s quality of life, and more specifically people with physical and mobility impairments. The robotic system is based on the Niryo-One robotic arm, equipped with a USB (Universal Serial Bus) HD (High Definition) camera on the end-effector. To achieve high accuracy, we modified the YOLO algorithm by adding novel features and additional computations to be used in the kinematic model. We evaluated the proposed system by conducting experiments using PhD students of our laboratory and demonstrated its effectiveness. The experimental results indicate that the robotic arm can detect and deliver the requested object in a timely manner with a 96.66% accuracy.
]]>Automation doi: 10.3390/automation2040014
Authors: Farzad Mohammadzadeh Shahir Meysam Gheisarnejad Mohammad-Hassan Khooban
In this paper, a new structure is proposed for a boost dc–dc converter based on the voltage-lift (VL) technique. The main advantages of the proposed converter are its lack of transformer, simple structure, free and low input current ripple, high voltage gain capability by using an input source, suitable voltage stress on semiconductors and lower output capacitance. Herein, the analysis of the proposed converter operating and its elements voltage and current relations in continuous conduction mode (CCM) and discontinuous conduction mode (DCM) are presented, and the voltage gain of each operating mode is individually calculated. Additionally, the critical inductance, current stress of switches, calculation of passive components’ values and efficiency are analyzed. In addition, the proposed converter is compared with other studied boost converters in terms of ideal voltage gain in the CCM and the number of active and passive components, maximum voltage stress on semiconductors, and situation of input current ripples. The correctness of the theoretical concepts is examined from the experimental results using the laboratory prototype.
]]>Automation doi: 10.3390/automation2030013
Authors: Jan Pennekamp Roman Matzutt Salil S. Kanhere Jens Hiller Klaus Wehrle
The Internet of Things provides manufacturing with rich data for increased automation. Beyond company-internal data exploitation, the sharing of product and manufacturing process data along and across supply chains enables more efficient production flows and product lifecycle management. Even more, data-based automation facilitates short-lived ad hoc collaborations, realizing highly dynamic business relationships for sustainable exploitation of production resources and capacities. However, the sharing and use of business data across manufacturers and with end customers add requirements on data accountability, verifiability, and reliability and needs to consider security and privacy demands. While research has already identified blockchain technology as a key technology to address these challenges, current solutions mainly evolve around logistics or focus on established business relationships instead of automated but highly dynamic collaborations that cannot draw upon long-term trust relationships. We identify three open research areas on the road to such a truly accountable and dependable manufacturing enabled by blockchain technology: blockchain-inherent challenges, scenario-driven challenges, and socio-economic challenges. Especially tackling the scenario-driven challenges, we discuss requirements and options for realizing a blockchain-based trustworthy information store and outline its use for automation to achieve a reliable sharing of product information, efficient and dependable collaboration, and dynamic distributed markets without requiring established long-term trust.
]]>Automation doi: 10.3390/automation2030012
Authors: Antonio Ribas Neto Julio Fajardo Willian Hideak Arita da Silva Matheus Kaue Gomes Maria Claudia Ferrari de Castro Eric Fujiwara Eric Rohmer
People taken by upper limb disorders caused by neurological diseases suffer from grip weakening, which affects their quality of life. Researches on soft wearable robotics and advances in sensor technology emerge as promising alternatives to develop assistive and rehabilitative technologies. However, current systems rely on surface electromyography and complex machine learning classifiers to retrieve the user intentions. In addition, the grasp assistance through electromechanical or fluidic actuators is passive and does not contribute to the rehabilitation of upper-limb muscles. Therefore, this paper presents a robotic glove integrated with a force myography sensor. The glove-like orthosis features tendon-driven actuation through servo motors, working as an assistive device for people with hand disabilities. The detection of user intentions employs an optical fiber force myography sensor, simplifying the operation beyond the usual electromyography approach. Moreover, the proposed system applies functional electrical stimulation to activate the grasp collaboratively with the tendon mechanism, providing motion support and assisting rehabilitation.
]]>Automation doi: 10.3390/automation2030011
Authors: Alireza Modir Ibrahim Tansel
Additive manufacturing (AM) applications have been steadily increasing in many industry sectors. AM allows creating complex geometries inside of a part to leave some space empty, called infills. Lighter parts are manufactured in a shorter time with less warpage if the strength of the part meets the design requirements. While the benefits of structural health monitoring (SHM) have been proven in different structures, few studies have investigated SHM methods on AM parts. In this study, the relationship between wave propagation and infill density has been studied for the additively manufactured polymer parts. The propagation of surface waves is monitored by using piezoelectric elements. Four rectangular parts are manufactured by using the material extrusion method with 20%, 40%, 60%, and 100% rectilinear infill densities. Four piezoelectric elements were attached on the surface of each beam, one for excitation and three for monitoring the response of the part at equal distances on each part. The results demonstrated that the surface waves diminish faster at parts with lower densities. The received signal in the part with totally solid infills showed about 10 times higher amplitudes compare with the part with 20% infill. The surface response to excitation (SuRE) method was used for sensing the loading on the part. Also, the wave propagation speed was calculated with exciting parts with a pulse signal with a 10-microsecond duration. The wave propagation speed was almost the same for all infill densities.
]]>Automation doi: 10.3390/automation2030010
Authors: Vasilis Androulakis Steven Schafrik Joseph Sottile Zach Agioutantis
In recent years, autonomous solutions in the multidisciplinary field of mining engineering have been an extremely popular applied research topic. This is a result of the increasing demands of society on mineral resources along with the accelerating exploitation of the currently economically viable resources, which lead the mining sector to turn to deeper, more-difficult-to-mine orebodies. An appropriate data management system comprises a crucial aspect of the designing and the engineering of a system that involves autonomous or semiautonomous vehicles. The vast volume of data collected from onboard sensors, as well as from a potential IoT network dispersed around a smart mine, necessitates the development of a reliable data management strategy. Ideally, this strategy will allow for fast and asynchronous access to the data for real-time processing and decision-making purposes as well as for visualization through a corresponding human–machine interface. The proposed system has been developed for autonomous navigation of a coalmine shuttle car and has been implemented on a 1/6th scale shuttle car in a mock mine. It comprises three separate nodes, namely, a data collection node, a data management node, and a data processing and visualization node. This approach was dictated by the large amount of collected data and the need to ensure uninterrupted and fast data management and flow. The implementation of an SQL database server allows for asynchronous, real-time, and reliable data management, including data storage and retrieval. On the other hand, this approach introduces latencies between the data management node and the other two nodes. In general, these latencies include sensor latencies, network latencies, and processing latencies. However, the data processing and visualization module is able to retrieve and process the latest data and make a decision about the next optimal movement of the shuttle car prototype in less than 900 ms. This allows the prototype to navigate efficiently around the pillars without interruptions.
]]>Automation doi: 10.3390/automation2030009
Authors: Igor Henrique Beloti Pizetta Alexandre Santos Brandão Mário Sarcinelli-Filho
This paper deals with a non-contact method to identify the aerodynamic propeller constants of the Parrot AR.Drone quadrotor. The experimental setup consists of a microphone installed in the flight arena to record audio data. In terms of methodology, a spectrogram analysis is adopted to estimate the propeller velocity based on the filtered sound signal. It is known that, in a hovering maneuver, when the UAV mass increases, the propellers rotate faster to produce the necessary thrust increment. In this work, the rotorcraft takes off with its factory settings, first with no hull, corresponding to a mass of 413 g, and after with a small hull, corresponding to a mass of 444 g, and a bigger hull, corresponding to a mass of 462 g. In the sequence, the velocity of the propellers are estimated for each of these three cases using spectrograms of audio recorded by a microphone, corresponding to the sound generated by the four rotors. Finally, the estimated velocity is used to identify the aerodynamic parameters, thus validating the proposal.
]]>Automation doi: 10.3390/automation2030008
Authors: Jorge Antonio Sarapura Flavio Roberti Ricardo Carelli
In the present work, we develop an adaptive dynamic controller based on monocular vision for the tracking of objects with a three-degrees of freedom (DOF) Scara robot manipulator. The main characteristic of the proposed control scheme is that it considers the robot dynamics, the depth of the moving object, and the mounting of the fixed camera to be unknown. The design of the control algorithm is based on an adaptive kinematic visual servo controller whose objective is the tracking of moving objects even with uncertainties in the parameters of the camera and its mounting. The design also includes a dynamic controller in cascade with the former one whose objective is to compensate the dynamics of the manipulator by generating the final control actions to the robot even with uncertainties in the parameters of its dynamic model. Using Lyapunov’s theory, we analyze the two proposed adaptive controllers for stability properties, and, through simulations, the performance of the complete control scheme is shown.
]]>Automation doi: 10.3390/automation2030007
Authors: Nikolaos Baras Antonios Chatzisavvas Dimitris Ziouzios Minas Dasygenis
It is evident that over the last years, the usage of robotics in warehouses has been rapidly increasing. The usage of robot vehicles in storage facilities has resulted in increased efficiency and improved productivity levels. The robots, however, are only as efficient as the algorithms that govern them. Many researchers have attempted to improve the efficiency of industrial robots by improving on the internal routing of a warehouse, or by finding the best locations for charging power stations. Because of the popularity of the problem, many research works can be found in the literature regarding warehouse routing. The majority of these algorithms found in the literature, however, are statically designed and cannot handle multi-robot situations, especially when robots have different characteristics. The proposed algorithm of this paper attempts to give the following solution to this issue: utilizing more than one robot simultaneously to allocate tasks and tailor the navigation path of each robot based on its characteristics, such as its speed, type and current location within the warehouse so as to minimize the task delivery timing. Moreover, the algorithm finds the optimal location for the placement of power stations. We evaluated the proposed methodology in a synthetic realistic environment and demonstrated that the algorithm is capable of finding an improved solution within a realistic time frame.
]]>Automation doi: 10.3390/automation2030006
Authors: Sebastian Sanchez Pranav A. Bhounsule
A rimless wheel or a wheel without a rim, is the simplest example of a legged robot and is an ideal testbed to understand the mechanics of locomotion. This paper presents the design, modeling, and control of a differential drive rimless wheel robot that achieves straight-line movement and turning. The robot design comprises a central axis with two 10-spoked springy rimless wheels on either side and a central body that houses the electronics, motors, transmission, computers, and batteries. To move straight, both motors are commanded to constant pitch control of the central body. To turn while maintaining constant pitch, a differential current is added and subtracted from currents on either motor. In separate tests, the robot achieved the maximum speed of 4.3 m per sec (9.66 miles per hour), the lowest total cost of transport (power per unit weight per unit velocity) of 0.13, and a smallest turning radius of 0.5 m. A kinematics-based model for steering and a dynamics-based sagittal (fore-aft) plane model for forward movement is presented. Finally, parameters studies that influence the speed, torque, power, and energetics of locomotion are performed. A rimless wheel that can move straight and turn can potentially be used to navigate in constrained spaces such as homes and offices.
]]>Automation doi: 10.3390/automation2030005
Authors: Mark Spiller Dirk Söffker
In turbomachines, dry friction resulting from stator–rotor contacts is a severe problem that may degrade lifetime of the machine or even lead to complete failure. Knowledge about the system states and contact forces is beneficial for system monitoring or to prevent contacts through, e.g., active magnetic bearings. In this paper, a nonlinear model is derived that describes the lateral rotor vibrations in the case of contact and no contact. The elastic behavior of the shaft is modeled based on the finite-element method. The contact is described by a dry friction model. An augmented system description is formulated that allows estimation of rotor displacements and contact forces by means of nonlinear filtering approaches like an extended Kalman filter. A simulation study was conducted that explicitly considered the hazardous backward whirl. The suggested approach shows suitable estimation performance related to both state and contact force estimation.
]]>Automation doi: 10.3390/automation2020004
Authors: Tiago Coito Bernardo Firme Miguel S. E. Martins Susana M. Vieira João Figueiredo João M. C. Sousa
The simultaneous integration of information from sensors with business data and how to acquire valuable information can be challenging. This paper proposes the simultaneous integration of information from sensors and business data. The proposal is supported by an industrial implementation, which integrates intelligent sensors and real-time decision-making, using a combination of PLC and PC Platforms in a three-level architecture: cloud-fog-edge. Automatic identification intelligent sensors are used to improve the decision-making of a dynamic scheduling tool. The proposed platform is applied to an industrial use-case in analytical Quality Control (QC) laboratories. The regulatory complexity, the personalized production, and traceability requirements make QC laboratories an interesting use case. We use intelligent sensors for automatic identification to improve the decision-making of a dynamic scheduling tool. Results show how the integration of intelligent sensors can improve the online scheduling of tasks. Estimations from system processing times decreased by over 30%. The proposed solution can be extended to other applications such as predictive maintenance, chemical industry, and other industries where scheduling and rescheduling are critical factors for the production.
]]>Automation doi: 10.3390/automation2020003
Authors: Bhavyansh Mishra Robert Griffin Hakki Erhan Sevil
Visual simultaneous localization and mapping (VSLAM) is an essential technique used in areas such as robotics and augmented reality for pose estimation and 3D mapping. Research on VSLAM using both monocular and stereo cameras has grown significantly over the last two decades. There is, therefore, a need for emphasis on a comprehensive review of the evolving architecture of such algorithms in the literature. Although VSLAM algorithm pipelines share similar mathematical backbones, their implementations are individualized and the ad hoc nature of the interfacing between different modules of VSLAM pipelines complicates code reuseability and maintenance. This paper presents a software model for core components of VSLAM implementations and interfaces that govern data flow between them while also attempting to preserve the elements that offer performance improvements over the evolution of VSLAM architectures. The framework presented in this paper employs principles from model-driven engineering (MDE), which are used extensively in the development of large and complicated software systems. The presented VSLAM framework will assist researchers in improving the performance of individual modules of VSLAM while not having to spend time on system integration of those modules into VSLAM pipelines.
]]>Automation doi: 10.3390/automation2010002
Authors: Patric Skalecki Maximilian Sesselmann Sabrina Rechkemmer Thorsten Britz Andreas Großmann Harald Garrecht Oliver Sawodny
The enhancement of new quality criteria in highway construction is a key aspect to improving the construction process and lifetime of road. In particular, mobile laser scanning systems are nowadays able to provide realistic 3D elevation profiles of a road to detect anomalies. In this context, this study utilizes a high-accuracy high-speed mobile mapping vehicle and evaluates a weighted longitudinal profile as an improved measure for evenness analysis. For comparison a classical method with a rolling straight edge was evaluated on the same road section and observed effects are discussed. The second focus is the areal reconstruction of the road thickness. For this purpose, a modern method was developed to spatially synchronize two high-speed laser scans using reference boxes next to the road, to transfer the point clouds into a surface model and to calculate the layer thickness. This procedure was conceptually validated by some pointwise measurements of the layer thickness. With this information, imperfections in the base layer could be detected automatically over a wide area at an early stage and countermeasures might be initiated before constructing the highway.
]]>Automation doi: 10.3390/automation2010001
Authors: Jazmín Zenteno-Torres Jérôme Cieslak Jorge Dávila David Henry
This paper is prepared within a collaboration between the Instituto Politécnico Nacional, which is a Mexican research institute that manages research on sliding-mode control theory, and the ARIA research team of the Intégration du Matériau au Système Lab., a French research group that engages research on model-based fault diagnosis and fault-tolerant control theories. The paper reviews the application of sliding mode control techniques to fault tolerant control and provides perspectives leading to posing some open problems. Operating principles, definitions of the basic concepts are recalled along with the control objectives and design procedures. The evolution of the sliding mode control technique through five generations (as classified by Fridman, Moreno and co-workers) is reviewed. Their respective design procedures, limitations, and robustness properties are also highlighted. The application of the five generations of sliding-mode controllers to fault-tolerant control is discussed. The focus is on some open problems that are judged to commonly be overlooked. Some applications in real-world systems are also presented.
]]>Automation doi: 10.3390/automation1010006
Authors: Ruth David Sandra Rothe Dirk Söffker
Research in understanding human behavior is a growing field within the development of Advanced Driving Assistance Systems (ADASs). In this contribution, a state machine approach is proposed to develop a driving behavior recognition model. The state machine approach is a behavior model based on the current state and a given set of inputs. Transitions to different states occur or we remain in the same state producing outputs. The transition between states depends on a set of environmental and driving variables. Based on a heuristic understanding of driving situations modeled as states, as well as one of the related actions modeling the state, using an assumed relation between them as the state machine topology, in this paper, a crisp approach is applied to adapt the model to real behaviors. An important aspect of the contribution is to introduce a trainable state machine-based model to describe drivers’ lane changing behavior. Three driving maneuvers are defined as states. The training of the model is related to the definition/tuning of transition variables (and state definitions). Here, driving data are used as the input for training. The non-dominated sorting genetic algorithm II is used to generate the optimized transition threshold. Comparing the data of actual human driving behaviors collected using driving simulator experiments and the calculated driving behaviors, this approach is able to develop a personalized behavior recognition model. The newly established algorithm presents an easy to apply, reliable, and interpretable AI approach.
]]>Automation doi: 10.3390/automation1010005
Authors: Eyad H. Abed
It is a sincere pleasure to welcome you to the inaugural issue of Automation [...]
]]>Automation doi: 10.3390/automation1010004
Authors: Viorel Minzu
Optimal control problems can be solved by a metaheuristic based algorithm (MbA) that yields an open-loop solution. The receding horizon control mechanism can integrate an MbA to produce a closed-loop solution. When the performance index includes a term depending on the final state (terminal penalty), the prediction’s time possibly surpasses a sampling period. This paper aims to avoid predicting the terminal penalty. The sequence of the best solution’s state variables becomes a reference trajectory; this one is used by a tracking structure that includes the real process, a process model (PM) and a tracking controller (TC). The reference trajectory must be followed up as much as possible by the real trajectory. The TC makes a one-step-ahead prediction and calculates the control inputs through a minimization procedure. Therefore the terminal penalty’s calculation is avoided. An example of a tracking structure is presented. The TC may also use an MbA for its minimization procedure. The implementation is presented in two versions: using a simulated annealing algorithm and an evolutionary algorithm. The simulations have proved that the proposed approach is realistic. The tracking structure does or does not work well, depending on the PM’s accuracy in reproducing the real process.
]]>Automation doi: 10.3390/automation1010003
Authors: Dario Pasqualotto Fabio Tinazzi Mauro Zigliotto
Synchronous reluctance motors are arousing lively interest as a possible alternative to the less efficient induction motors. An open issue is the effective tuning of the inner current loops because of the nonlinearity that cannot be overlooked. The present paper uses a relay feedback approach to perform autotuning without resorting to any parameter knowledge. The tuning is iterated at different working points, to get a uniform current control bandwidth everywhere. Unlike many solutions in the field, the algorithm is truly autonomous, in the sense that it also suggests a correct value for the bandwidth specification. The paper includes both simulation and experimental results, obtained on a laboratory prototype.
]]>Automation doi: 10.3390/automation1010002
Authors: Thomas Kent Anthony Pipe Arthur Richards Jim Hutchinson Wolfgang Schuster
VENTURER was one of the first three UK government funded research and innovation projects on Connected Autonomous Vehicles (CAVs) and was conducted predominantly in the South West region of the country. A series of increasingly complex scenarios conducted in an urban setting were used to: (i) evaluate the technology created as a part of the project; (ii) systematically assess participant responses to CAVs and; (iii) inform the development of potential insurance models and legal frameworks. Developing this understanding contributed key steps towards facilitating the deployment of CAVs on UK roads. This paper aims to describe the VENTURER Project trials, their objectives and detail some of the key technologies used. Importantly we aim to introduce some informative challenges that were overcame and the subsequent project and technological lessons learned in a hope to help others plan and execute future CAV research. The project successfully integrated several technologies crucial to CAV development. These included, a Decision Making System using behaviour trees to make high level decisions; A pilot-control system to smoothly and comfortably turn plans into throttle and steering actuation; Sensing and perception systems to make sense of raw sensor data; Inter-CAV Wireless communication capable of demonstrating vehicle-to-vehicle communication of potential hazards. The closely coupled technology integration, testing and participant-focused trial schedule led to a greatly improved understanding of the engineering and societal barriers that CAV development faces. From a behavioural standpoint the importance of reliability and repeatability far outweighs a need for novel trajectories, while the sensor-to-perception capabilities are critical, the process of verification and validation is extremely time consuming. Additionally, the added capabilities that can be leveraged from inter-CAV communications shows the potential for improved road safety that could result. Importantly, to effectively conduct human factors experiments in the CAV sector under consistent and repeatable conditions, one needs to define a scripted and stable set of scenarios that uses reliable equipment and a controllable environmental setting. This requirement can often be at odds with making significant technology developments, and if both are part of a project’s goals then they may need to be separated from each other.
]]>Automation doi: 10.3390/automation1010001
Authors: Mark Aull Andy Stough Kelly Cohen
Traditional on-shore horizontal-axis wind turbines need to be large for both performance reasons (e.g., clearing ground turbulence and reaching higher wind speeds) and for economic reasons (e.g., more efficient land use, lower maintenance costs, and fewer controllers and grid attachments) while their efficiency is scale and mass independent. Airborne wind energy (AWE) system efficiency is a function of system size and AWE system operating altitude is less directly coupled to system power rating. This paper derives fly-gen AWE system parameters from small number of design parameters, which are used to optimize a design for energy cost. This paper then scales AWE systems and optimizes them at each scale to determine the relationships between size, efficiency, power output, and cost. The results indicate that physics and economics favor a larger number of small units, at least offshore or where land cost is small.
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