Editorial

6 pages, 198 KiB

Open AccessEditorial

by Luige Vladareanu, Hongnian Yu, Hongbo Wang and Yongfei Feng

Sensors 2023, 23(12), 5699; https://doi.org/10.3390/s23125699 - 19 Jun 2023

Cited by 1 | Viewed by 1105

Advanced intelligent control (AIC) is a rapidly evolving and complex field that poses significant challenges [...] Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

Research

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18 pages, 7492 KiB

Open AccessArticle

Implementation of a Real-Time Object Pick-and-Place System Based on a Changing Strategy for Rapidly-Exploring Random Tree

by Ching-Chang Wong, Chong-Jia Chen, Kai-Yi Wong and Hsuan-Ming Feng

Sensors 2023, 23(10), 4814; https://doi.org/10.3390/s23104814 - 16 May 2023

Cited by 3 | Viewed by 1438

Abstract

An object pick-and-place system with a camera, a six-degree-of-freedom (DOF) robot manipulator, and a two-finger gripper is implemented based on the robot operating system (ROS) in this paper. A collision-free path planning method is one of the most fundamental problems that has to [...] Read more.

An object pick-and-place system with a camera, a six-degree-of-freedom (DOF) robot manipulator, and a two-finger gripper is implemented based on the robot operating system (ROS) in this paper. A collision-free path planning method is one of the most fundamental problems that has to be solved before the robot manipulator can autonomously pick-and-place objects in complex environments. In the implementation of the real-time pick-and-place system, the success rate and computing time of path planning by a six-DOF robot manipulator are two essential key factors. Therefore, an improved rapidly-exploring random tree (RRT) algorithm, named changing strategy RRT (CS-RRT), is proposed. Based on the method of gradually changing the sampling area based on RRT (CSA-RRT), two mechanisms are used in the proposed CS-RRT to improve the success rate and computing time. The proposed CS-RRT algorithm adopts a sampling-radius limitation mechanism, which enables the random tree to approach the goal area more efficiently each time the environment is explored. It can avoid spending a lot of time looking for valid points when it is close to the goal point, thus reducing the computing time of the improved RRT algorithm. In addition, the CS-RRT algorithm adopts a node counting mechanism, which enables the algorithm to switch to an appropriate sampling method in complex environments. It can avoid the search path being trapped in some constrained areas due to excessive exploration in the direction of the goal point, thus improving the adaptability of the proposed algorithm to various environments and increasing the success rate. Finally, an environment with four object pick-and-place tasks is established, and four simulation results are given to illustrate that the proposed CS-RRT-based collision-free path planning method has the best performance compared with the other two RRT algorithms. A practical experiment is also provided to verify that the robot manipulator can indeed complete the specified four object pick-and-place tasks successfully and effectively. Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

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35 pages, 13762 KiB

Open AccessArticle

A Hybrid Stacked CNN and Residual Feedback GMDH-LSTM Deep Learning Model for Stroke Prediction Applied on Mobile AI Smart Hospital Platform

by Bassant M. Elbagoury, Luige Vladareanu, Victor Vlădăreanu, Abdel Badeeh Salem, Ana-Maria Travediu and Mohamed Ismail Roushdy

Sensors 2023, 23(7), 3500; https://doi.org/10.3390/s23073500 - 27 Mar 2023

Cited by 13 | Viewed by 2844

Abstract

Artificial intelligence (AI) techniques for intelligent mobile computing in healthcare has opened up new opportunities in healthcare systems. Combining AI techniques with the existing Internet of Medical Things (IoMT) will enhance the quality of care that patients receive at home remotely and the [...] Read more.

Artificial intelligence (AI) techniques for intelligent mobile computing in healthcare has opened up new opportunities in healthcare systems. Combining AI techniques with the existing Internet of Medical Things (IoMT) will enhance the quality of care that patients receive at home remotely and the successful establishment of smart living environments. Building a real AI for mobile AI in an integrated smart hospital environment is a challenging problem due to the complexities of receiving IoT medical sensors data, data analysis, and deep learning algorithm complexity programming for mobile AI engine implementation AI-based cloud computing complexities, especially when we tackle real-time environments of AI technologies. In this paper, we propose a new mobile AI smart hospital platform architecture for stroke prediction and emergencies. In addition, this research is focused on developing and testing different modules of integrated AI software based on XAI architecture, this is for the mobile health app as an independent expert system or as connected with a simulated environment of an AI-cloud-based solution. The novelty is in the integrated architecture and results obtained in our previous works and this extended research on hybrid GMDH and LSTM deep learning models for the proposed artificial intelligence and IoMT engine for mobile health edge computing technology. Its main goal is to predict heart–stroke disease. Current research is still missing a mobile AI system for heart/brain stroke prediction during patient emergency cases. This research work implements AI algorithms for stroke prediction and diagnosis. The hybrid AI in connected health is based on a stacked CNN and group handling method (GMDH) predictive analytics model, enhanced with an LSTM deep learning module for biomedical signals prediction. The techniques developed depend on the dataset of electromyography (EMG) signals, which provides a significant source of information for the identification of normal and abnormal motions in a stroke scenario. The resulting artificial intelligence mHealth app is an innovation beyond the state of the art and the proposed techniques achieve high accuracy as stacked CNN reaches almost 98% for stroke diagnosis. The GMDH neural network proves to be a good technique for monitoring the EMG signal of the same patient case with an average accuracy of 98.60% to an average of 96.68% of the signal prediction. Moreover, extending the GMDH model and a hybrid LSTM with dense layers deep learning model has improved significantly the prediction results that reach an average of 99%. Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

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39 pages, 22529 KiB

Open AccessArticle

Improvement of Hexacopter UAVs Attitude Parameters Employing Control and Decision Support Systems

by Mihai-Alin Stamate, Cristina Pupăză, Florin-Adrian Nicolescu and Cristian-Emil Moldoveanu

Sensors 2023, 23(3), 1446; https://doi.org/10.3390/s23031446 - 28 Jan 2023

Cited by 5 | Viewed by 4208

Abstract

Today, there is a conspicuous upward trend for the development of unmanned aerial vehicles (UAVs), especially in the field of multirotor drones. Their advantages over fixed-wing aircrafts are that they can hover, which allows their usage in a wide range of remote surveillance [...] Read more.

Today, there is a conspicuous upward trend for the development of unmanned aerial vehicles (UAVs), especially in the field of multirotor drones. Their advantages over fixed-wing aircrafts are that they can hover, which allows their usage in a wide range of remote surveillance applications: industrial, strategic, governmental, public and homeland security. Moreover, because the component market for this type of vehicles is in continuous growth, new concepts have emerged to improve the stability and reliability of the multicopters, but efficient solutions with reduced costs are still expected. This work is focused on hexacopter UAV tests carried out on an original platform both within laboratory and on unrestricted open areas during the start–stop manoeuvres of the motors to verify the operational parameters, hover flight, the drone stability and reliability, as well as the aerodynamics and robustness at different wind speeds. The flight parameters extracted from the sensor systems’ comprising accelerometers, gyroscopes, magnetometers, barometers, GPS antenna and EO/IR cameras were analysed, and adjustments were performed accordingly, when needed. An FEM simulation approach allowed an additional decision support platform that expanded the experiments in the virtual environment. Finally, practical conclusions were drawn to enhance the hexacopter UAV stability, reliability and manoeuvrability. Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

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15 pages, 4536 KiB

Open AccessArticle

Detecting Machining Defects inside Engine Piston Chamber with Computer Vision and Machine Learning

by Marian Marcel Abagiu, Dorian Cojocaru, Florin Manta and Alexandru Mariniuc

Sensors 2023, 23(2), 785; https://doi.org/10.3390/s23020785 - 10 Jan 2023

Cited by 5 | Viewed by 3035

Abstract

This paper describes the implementation of a solution for detecting the machining defects from an engine block, in the piston chamber. The solution was developed for an automotive manufacturer and the main goal of the implementation is the replacement of the visual inspection [...] Read more.

This paper describes the implementation of a solution for detecting the machining defects from an engine block, in the piston chamber. The solution was developed for an automotive manufacturer and the main goal of the implementation is the replacement of the visual inspection performed by a human operator with a computer vision application. We started by exploring different machine vision applications used in the manufacturing environment for several types of operations, and how machine learning is being used in robotic industrial applications. The solution implementation is re-using hardware that is already available at the manufacturing plant and decommissioned from another system. The re-used components are the cameras, the IO (Input/Output) Ethernet module, sensors, cables, and other accessories. The hardware will be used in the acquisition of the images, and for processing, a new system will be implemented with a human–machine interface, user controls, and communication with the main production line. Main results and conclusions highlight the efficiency of the CCD (charged-coupled device) sensors in the manufacturing environment and the robustness of the machine learning algorithms (convolutional neural networks) implemented in computer vision applications (thresholding and regions of interest). Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

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17 pages, 4873 KiB

Open AccessArticle

Synchronous Control of a Group of Flying Robots Following a Leader UAV in an Unfamiliar Environment

by Konrad Wojtowicz and Przemysław Wojciechowski

Sensors 2023, 23(2), 740; https://doi.org/10.3390/s23020740 - 09 Jan 2023

Cited by 2 | Viewed by 1591

Abstract

An increasing number of professional drone flights require situational awareness of aerial vehicles. Vehicles in a group of drones must be aware of their surroundings and the other group members. The amount of data to be exchanged and the total cost are skyrocketing. [...] Read more.

An increasing number of professional drone flights require situational awareness of aerial vehicles. Vehicles in a group of drones must be aware of their surroundings and the other group members. The amount of data to be exchanged and the total cost are skyrocketing. This paper presents an implementation and assessment of an organized drone group comprising a fully aware leader and much less expensive followers. The solution achieved a significant cost reduction by decreasing the number of sensors onboard followers and improving the organization and manageability of the group in the system. In this project, a group of quadrotor drones was evaluated. An automatically flying leader was followed by drones equipped with low-end cameras only. The followers were tasked with following ArUco markers mounted on a preceding drone. Several test tasks were designed and conducted. Finally, the presented system proved appropriate for slowly moving groups of drones. Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

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16 pages, 1843 KiB

Open AccessArticle

Spherical Wrist Manipulator Local Planner for Redundant Tasks in Collaborative Environments

by Marcello Chiurazzi, Joan Ortega Alcaide, Alessandro Diodato, Arianna Menciassi and Gastone Ciuti

Sensors 2023, 23(2), 677; https://doi.org/10.3390/s23020677 - 06 Jan 2023

Cited by 2 | Viewed by 1573

Abstract

Standard industrial robotic manipulators use well-established high performing technologies. However, such manipulators do not guarantee a safe Human–Robot Interaction (HRI), limiting their usage in industrial and medical applications. This paper proposes a novel local path planner for spherical wrist manipulators to control the [...] Read more.

Standard industrial robotic manipulators use well-established high performing technologies. However, such manipulators do not guarantee a safe Human–Robot Interaction (HRI), limiting their usage in industrial and medical applications. This paper proposes a novel local path planner for spherical wrist manipulators to control the execution of tasks where the manipulator number of joints is redundant. Such redundancy is used to optimize robot motion and dexterity. We present an intuitive parametrization of the end-effector (EE) angular motion, which decouples the rotation of the third joint of the wrist from the rest of the angular motions. Manipulator EE motion is controlled through a decentralized linear system with closed-loop architecture. The local planner integrates a novel collision avoidance strategy based on a potential repulsive vector applied to the EE. Contrary to classic potential field approaches, the collision avoidance algorithm considers the entire manipulator surface, enhancing human safety. The local path planner is simulated in three generic scenarios: (i) following a periodic reference, (ii) a random sequence of step signal references, and (iii) avoiding instantly introduced obstacles. Time and frequency domain analysis demonstrated that the developed planner, aside from better parametrizing redundant tasks, is capable of successfully executing the simulated paths (max error = 0.25°) and avoiding obstacles. Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

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22 pages, 6081 KiB

Open AccessArticle

A Self-Collision Detection Algorithm of a Dual-Manipulator System Based on GJK and Deep Learning

by Di Wu, Zhi Yu, Alimasi Adili and Fanchen Zhao

Sensors 2023, 23(1), 523; https://doi.org/10.3390/s23010523 - 03 Jan 2023

Cited by 3 | Viewed by 2226

Abstract

Self-collision detection is fundamental to the safe operation of multi-manipulator systems, especially when cooperating in highly dynamic working environments. Existing methods still face the problem that detection efficiency and accuracy cannot be achieved at the same time. In this paper, we introduce artificial [...] Read more.

Self-collision detection is fundamental to the safe operation of multi-manipulator systems, especially when cooperating in highly dynamic working environments. Existing methods still face the problem that detection efficiency and accuracy cannot be achieved at the same time. In this paper, we introduce artificial intelligence technology into the control system. Based on the Gilbert-Johnson-Keerthi (GJK) algorithm, we generated a dataset and trained a deep neural network (DLNet) to improve the detection efficiency. By combining DLNet and the GJK algorithm, we propose a two-level self-collision detection algorithm (DLGJK algorithm) to solve real-time self-collision detection problems in a dual-manipulator system with fast-continuous and high-precision properties. First, the proposed algorithm uses DLNet to determine whether the current working state of the system has a risk of self-collision; since most of the working states in a system workspace do not have a self-collision risk, DLNet can effectively reduce the number of unnecessary detections and improve the detection efficiency. Then, for the working states with a risk of self-collision, we modeled precise colliders and applied the GJK algorithm for fine self-collision detection, which achieved detection accuracy. The experimental results showed that compared to that with the global use of the GJK algorithm for self-collision detection, the DLGJK algorithm can reduce the time expectation of a single detection in a system workspace by 97.7%. In the path planning of the manipulators, it could effectively reduce the number of unnecessary detections, improve the detection efficiency, and reduce system overhead. The proposed algorithm also has good scalability for a multi-manipulator system that can be split into dual-manipulator systems. Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

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22 pages, 5179 KiB

Open AccessArticle

Method of Changing Running Direction of Cheetah-Inspired Quadruped Robot

by Meng Ning, Jun Yang, Ziqiang Zhang, Jun Li, Zhi Wang, Longxing Wei and Pengjin Feng

Sensors 2022, 22(24), 9601; https://doi.org/10.3390/s22249601 - 07 Dec 2022

Cited by 5 | Viewed by 2059

Abstract

The rapid change of motion direction during running is beneficial to improving the movement flexibility of the quadruped robot, which is of great relevance to its research. How to make the robot change its motion direction during running and achieve good dynamic stability [...] Read more.

The rapid change of motion direction during running is beneficial to improving the movement flexibility of the quadruped robot, which is of great relevance to its research. How to make the robot change its motion direction during running and achieve good dynamic stability is a problem to be solved. In this paper, a method to change the running direction of the cheetah-inspired quadruped robot is proposed. Based on the analysis of the running of the cheetah, a dynamic model of the quadruped robot is established, and a two-level stability index system, including a minimum index system and a range index system, is proposed. On this basis, the objective function based on the stability index system and optimization variables, including leg landing points, trunk movement trajectory, and posture change rule, are determined. Through these constraints, the direction changes with good dynamic stability of the cheetah-inspired quadruped robot during running is realized by controlling the leg parameters. The robot will not roll over during high-speed movement. Finally, the correctness of the proposed method is proven by simulation. This paper provides a theoretical basis for the quadruped robot’s rapid change of direction in running. Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

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14 pages, 4029 KiB

Open AccessArticle

Prediction of Metal Additively Manufactured Surface Roughness Using Deep Neural Network

by Min Seop So, Gi Jeong Seo, Duck Bong Kim and Jong-Ho Shin

Sensors 2022, 22(20), 7955; https://doi.org/10.3390/s22207955 - 19 Oct 2022

Cited by 8 | Viewed by 2179

Abstract

In recent years, manufacturing industries (e.g., medical, aerospace, and automobile) have been changing their manufacturing process to small-quantity batch production to flexibly cope with fluctuations in demand. Therefore, many companies are trying to produce products by introducing 3D printing technology into the manufacturing [...] Read more.

In recent years, manufacturing industries (e.g., medical, aerospace, and automobile) have been changing their manufacturing process to small-quantity batch production to flexibly cope with fluctuations in demand. Therefore, many companies are trying to produce products by introducing 3D printing technology into the manufacturing process. The 3D printing process is based on additive manufacturing (AM), which can fabricate complex shapes and reduce material waste and production time. Although AM has many advantages, its product quality is poor compared to conventional manufacturing systems. This study proposes a methodology to improve the quality of AM products based on data analysis. The targeted quality of AM is the surface roughness of the stacked wall. Surface roughness is one of the important quality indicators and can cause short product life and poor structure performance. To control the surface roughness, the resultant surface roughness needs to be predicted in advance depending on the process parameters. Various analysis methods such as data pre-processing and deep neural networks (DNN) combined with sensor data are used to predict surface roughness in the proposed methodology. The proposed methodology is applied to field data from operated wire + arc additive manufacturing (WAAM), and the analysis result shows its effectiveness, with a mean absolute percentage error (MAPE) of 1.93%. Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

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19 pages, 6816 KiB

Open AccessArticle

sEMG-Based Gain-Tuned Compliance Control for the Lower Limb Rehabilitation Robot during Passive Training

by Junjie Tian, Hongbo Wang, Siyuan Zheng, Yuansheng Ning, Xingchao Zhang, Jianye Niu and Luige Vladareanu

Sensors 2022, 22(20), 7890; https://doi.org/10.3390/s22207890 - 17 Oct 2022

Cited by 3 | Viewed by 1777

Abstract

The lower limb rehabilitation robot is a typical man-machine coupling system. Aiming at the problems of insufficient physiological information and unsatisfactory safety performance in the compliance control strategy for the lower limb rehabilitation robot during passive training, this study developed a surface electromyography-based [...] Read more.

The lower limb rehabilitation robot is a typical man-machine coupling system. Aiming at the problems of insufficient physiological information and unsatisfactory safety performance in the compliance control strategy for the lower limb rehabilitation robot during passive training, this study developed a surface electromyography-based gain-tuned compliance control (EGCC) strategy for the lower limb rehabilitation robot. First, the mapping function relationship between the normalized surface electromyography (sEMG) signal and the gain parameter was established and an overall EGCC strategy proposed. Next, the EGCC strategy without sEMG information was simulated and analyzed. The effects of the impedance control parameters on the position correction amount were studied, and the change rules of the robot end trajectory, man-machine contact force, and position correction amount analyzed in different training modes. Then, the sEMG signal acquisition and feature analysis of target muscle groups under different training modes were carried out. Finally, based on the lower limb rehabilitation robot control system, the influence of normalized sEMG threshold on the robot end trajectory and gain parameters under different training modes was experimentally studied. The simulation and experimental results show that the adoption of the EGCC strategy can significantly enhance the compliance of the robot end-effector by detecting the sEMG signal and improve the safety of the robot in different training modes, indicating the EGCC strategy has good application prospects in the rehabilitation robot field. Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

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15 pages, 4590 KiB

Open AccessArticle

Human–Robot Cooperative Strength Training Based on Robust Admittance Control Strategy

by Musong Lin, Hongbo Wang, Congliang Yang, Wenjie Liu, Jianye Niu and Luige Vladareanu

Sensors 2022, 22(20), 7746; https://doi.org/10.3390/s22207746 - 12 Oct 2022

Cited by 3 | Viewed by 1467

Abstract

A stroke is a common disease that can easily lead to lower limb motor dysfunction in the elderly. Stroke survivors can effectively train muscle strength through leg flexion and extension training. However, available lower limb rehabilitation robots ignore the knee soft tissue protection [...] Read more.

A stroke is a common disease that can easily lead to lower limb motor dysfunction in the elderly. Stroke survivors can effectively train muscle strength through leg flexion and extension training. However, available lower limb rehabilitation robots ignore the knee soft tissue protection of the elderly in training. This paper proposes a human–robot cooperative lower limb active strength training based on a robust admittance control strategy. The stiffness change law of the admittance model is designed based on the biomechanics of knee joints, and it can guide the user to make force correctly and reduce the stress on the joint soft tissue. The controller will adjust the model stiffness in real-time according to the knee joint angle and then indirectly control the exertion force of users. This control strategy not only can avoid excessive compressive force on the joint soft tissue but also can enhance the stimulation of quadriceps femoris muscles. Moreover, a dual input robust control is proposed to improve the tracking performance under the disturbance caused by model uncertainty, interaction force and external noise. Experiments about the controller performance and the training feasibility were conducted with eight stroke survivors. Results show that the designed controller can effectively influence the interaction force; it can reduce the possibility of joint soft tissue injury. The robot also has a good tracking performance under disturbances. This control strategy also can enhance the stimulation of quadriceps femoris muscles, which is proved by measuring the muscle electrical signal and interaction force. Human–robot cooperative strength training is a feasible method for training lower limb muscles with the knee soft tissue protection mechanism. Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

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24 pages, 5642 KiB

Open AccessArticle

Navigation Path Based Universal Mobile Manipulator Integrated Controller (NUMMIC)

by Taehyeon Kim, Myunghyun Kim, Sungwoo Yang and Donghan Kim

Sensors 2022, 22(19), 7369; https://doi.org/10.3390/s22197369 - 28 Sep 2022

Cited by 2 | Viewed by 1842

Abstract

As the demand for service robots increases, a mobile manipulator robot which can perform various tasks in a dynamic environment attracts great attention. There are some controllers that control mobile platform and manipulator arm simultaneously for efficient performance, but most of them are [...] Read more.

As the demand for service robots increases, a mobile manipulator robot which can perform various tasks in a dynamic environment attracts great attention. There are some controllers that control mobile platform and manipulator arm simultaneously for efficient performance, but most of them are difficult to apply universally since they are based on only one mobile manipulator model. This lack of versatility can be a big problem because most mobile manipulator robots are made by connecting a mobile platform and manipulator from different companies. To overcome this problem, this paper proposes a simultaneous controller which can be applied not only to one model but also to various types of mobile manipulator robots. The proposed controller has three main characteristics, which are as follows: (1) establishing a pose that motion planning can be carried out in any position, avoiding obstacles and stopping in a stable manner at the target coordinates, (2) preventing the robot from collision with surrounding obstacles while driving, (3) defining a safety area where the manipulator does not hit the obstacles while driving and executing the manipulation accordingly. Our controller is fully compatible with Robot Operating System (ROS) and has been used successfully with three different types of mobile manipulator robots. In addition, we conduct motion planning experiments on five targets, each in two simulation worlds, and two motion planning scenarios using real robots in real-world environments. The result shows a significant improvement in time compared to existing control methods in various types of mobile manipulator and demonstrates that the controller works successfully in the real environment. The proposed controller is available on GitHub. Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

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16 pages, 8249 KiB

Open AccessArticle

Research on Monocular-Vision-Based Finger-Joint-Angle-Measurement System

by Yongfei Feng, Mingwei Zhong and Fangyan Dong

Sensors 2022, 22(19), 7276; https://doi.org/10.3390/s22197276 - 26 Sep 2022

Cited by 1 | Viewed by 2632

Abstract

The quantitative measurement of finger-joint range of motion plays an important role in assessing the level of hand disability and intervening in the treatment of patients. An industrial monocular-vision-based knuckle-joint-activity-measurement system is proposed with short measurement time and the simultaneous measurement of multiple [...] Read more.

The quantitative measurement of finger-joint range of motion plays an important role in assessing the level of hand disability and intervening in the treatment of patients. An industrial monocular-vision-based knuckle-joint-activity-measurement system is proposed with short measurement time and the simultaneous measurement of multiple joints. In terms of hardware, the system can adjust the light-irradiation angle and the light-irradiation intensity of the marker by actively adjusting the height of the light source to enhance the difference between the marker and the background and reduce the difficulty of segmenting the target marker and the background. In terms of algorithms, a combination of multiple-vision algorithms is used to compare the image-threshold segmentation and Hough outer- and inner linear detection as the knuckle-activity-range detection method of the system. To verify the accuracy of the visual-detection method, nine healthy volunteers were recruited for experimental validation, and the experimental results showed that the average angular deviation in the flexion/extension of the knuckle was 0.43° at the minimum and 0.59° at the maximum, and the average angular deviation in the adduction/abduction of the knuckle was 0.30° at the minimum and 0.81° at the maximum, which were all less than 1°. In the multi-angle velocimetry experiment, the time taken by the system was much less than that taken by the conventional method. Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

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13 pages, 3767 KiB

Open AccessArticle

Smart Vehicle Path Planning Based on Modified PRM Algorithm

by Qiongqiong Li, Yiqi Xu, Shengqiang Bu and Jiafu Yang

Sensors 2022, 22(17), 6581; https://doi.org/10.3390/s22176581 - 31 Aug 2022

Cited by 19 | Viewed by 2067

Abstract

Path planning is a very important step for mobile smart vehicles in complex environments. Sampling based planners such as the Probabilistic Roadmap Method (PRM) have been widely used for smart vehicle applications. However, there exist some shortcomings, such as low efficiency, low reuse [...] Read more.

Path planning is a very important step for mobile smart vehicles in complex environments. Sampling based planners such as the Probabilistic Roadmap Method (PRM) have been widely used for smart vehicle applications. However, there exist some shortcomings, such as low efficiency, low reuse rate of the roadmap, and a lack of guidance in the selection of sampling points. To solve the above problems, we designed a pseudo-random sampling strategy with the main spatial axis as the reference axis. We optimized the generation of sampling points, removed redundant sampling points, set the distance threshold between road points, adopted a two-way incremental method for collision detections, and optimized the number of collision detection calls to improve the construction efficiency of the roadmap. The key road points of the planned path were extracted as discrete control points of the Bessel curve, and the paths were smoothed to make the generated paths more consistent with the driving conditions of vehicles. The correctness of the modified PRM was verified and analyzed using MATLAB and ROS to build a test platform. Compared with the basic PRM algorithm, the modified PRM algorithm has advantages related to speed in constructing the roadmap, path planning, and path length. Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

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21 pages, 7040 KiB

Open AccessArticle

Model Predictive Control of a Novel Wheeled–Legged Planetary Rover for Trajectory Tracking

by Jun He, Yanlong Sun, Limin Yang and Feng Gao

Sensors 2022, 22(11), 4164; https://doi.org/10.3390/s22114164 - 30 May 2022

Cited by 6 | Viewed by 2537

Abstract

Amid increasing demands for planetary exploration, wide-range autonomous exploration is still a great challenge for existing planetary rovers, which calls for new planetary rovers with novel locomotive mechanisms and corresponding control strategies. This paper proposes a novel wheeled–legged mechanism for the design of [...] Read more.

Amid increasing demands for planetary exploration, wide-range autonomous exploration is still a great challenge for existing planetary rovers, which calls for new planetary rovers with novel locomotive mechanisms and corresponding control strategies. This paper proposes a novel wheeled–legged mechanism for the design of planetary rovers. The leg suspension utilizes a rigid–flexible coupling mechanism with a hybrid serial–parallel topology. First, the kinematic model is derived. Then, a control strategy for the wheeled–legged rover that includes a trajectory tracking module based on the model predictive control, the steering strategy, and the wheel speed allocation algorithm is proposed. After that, three groups of cosimulations with different trajectories and speeds, and experiments are carried out. Results of both the simulations and experiments validate the proposed control method. Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

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27 pages, 10905 KiB

Open AccessArticle

The Hybrid Position/Force Walking Robot Control Using Extenics Theory and Neutrosophic Logic Decision

by Ionel-Alexandru Gal, Alexandra-Cătălina Ciocîrlan and Luige Vlădăreanu

Sensors 2022, 22(10), 3663; https://doi.org/10.3390/s22103663 - 11 May 2022

Cited by 2 | Viewed by 1722

Abstract

This paper presents a hybrid force/position control. We developed it for a hexapod walking robot that combines multiple bipedal robots to increase its load. The control method integrated Extenics theory with neutrosophic logic to obtain a two-stage decision-making algorithm. The first stage was [...] Read more.

This paper presents a hybrid force/position control. We developed it for a hexapod walking robot that combines multiple bipedal robots to increase its load. The control method integrated Extenics theory with neutrosophic logic to obtain a two-stage decision-making algorithm. The first stage was an offline qualitative decision-applying Extenics theory, and the second was a real-time decision process using neutrosophic logic and DSmT theory. The two-stage algorithm separated the control phases into a kinematic control method that used a PID regulator and a dynamic control method developed with the help of sliding mode control (SMC). By integrating both control methods separated by a dynamic switching algorithm, we obtained a hybrid force/position control that took advantage of both kinematic and dynamic control properties to drive a mobile walking robot. The experimental and predicted results were in good agreement. They indicated that the proposed hybrid control is efficient in using the two-stage decision algorithm to drive the hexapod robot motors using kinematic and dynamic control methods. The experiment presents the robot’s foot positioning error while walking. The results show how the switching method alters the system precision during the pendulum phase compared to the weight support phase, which can better compensate for the robot’s dynamic parameters. The proposed switching algorithm directly influences the overall control precision, while we aimed to obtain a fast switch with a lower impact on the control parameters. The results show the error on all axes and break it down into walking stages to better understand the control behavior and precision. Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

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16 pages, 1289 KiB

Open AccessArticle

Control Design for Uncertain Higher-Order Networked Nonlinear Systems via an Arbitrary Order Finite-Time Sliding Mode Control Law

by Maryam Munir, Qudrat Khan, Safeer Ullah, Tayyaba Maryam Syeda and Abdullah A. Algethami

Sensors 2022, 22(7), 2748; https://doi.org/10.3390/s22072748 - 02 Apr 2022

Cited by 6 | Viewed by 1443

Abstract

The authors proposed an arbitrary order finite-time sliding mode control (SMC) design for a networked of uncertain higher-order nonlinear systems. A network of

n + 1

nodes, connected via a directed graph (with fixed topology), is considered. The nodes are considered to be [...] Read more.

The authors proposed an arbitrary order finite-time sliding mode control (SMC) design for a networked of uncertain higher-order nonlinear systems. A network of

n + 1

nodes, connected via a directed graph (with fixed topology), is considered. The nodes are considered to be uncertain in nature. A consensus error-based canonical form of the error dynamics is developed and a new arbitrary order distributed control protocol design strategy is proposed, which not only ensures the sliding mode enforcement in finite time but also confirms the finite time error dynamics stability. Rigorous stability analysis, in closed-loop, is presented, and a simulation example is given, which demonstrates the results developed in this work. Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

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21 pages, 7286 KiB

Open AccessArticle

Intelligent Tracking of Mechanically Thrown Objects by Industrial Catching Robot for Automated In-Plant Logistics 4.0

by Nauman Qadeer, Jamal Hussain Shah, Muhammad Sharif, Muhammad Attique Khan, Ghulam Muhammad and Yu-Dong Zhang

Sensors 2022, 22(6), 2113; https://doi.org/10.3390/s22062113 - 09 Mar 2022

Cited by 15 | Viewed by 2688

Abstract

Industry 4.0 smart manufacturing systems are equipped with sensors, smart machines, and intelligent robots. The automated in-plant transportation of manufacturing parts through throwing and catching robots is an attempt to accelerate the transportation process and increase productivity by the optimized utilization of in-plant [...] Read more.

Industry 4.0 smart manufacturing systems are equipped with sensors, smart machines, and intelligent robots. The automated in-plant transportation of manufacturing parts through throwing and catching robots is an attempt to accelerate the transportation process and increase productivity by the optimized utilization of in-plant facilities. Such an approach requires intelligent tracking and prediction of the final 3D catching position of thrown objects, while observing their initial flight trajectory in real-time, by catching robot in order to grasp them accurately. Due to non-deterministic nature of such mechanically thrown objects’ flight, accurate prediction of their complete trajectory is only possible if we accurately observe initial trajectory as well as intelligently predict remaining trajectory. The thrown objects in industry can be of any shape but detecting and accurately predicting interception positions of any shape object is an extremely challenging problem that needs to be solved step by step. In this research work, we only considered spherical shape objects as their3D central position can be easily determined. Our work comprised of development of a 3D simulated environment which enabled us to throw object of any mass, diameter, or surface air friction properties in a controlled internal logistics environment. It also enabled us to throw object with any initial velocity and observe its trajectory by placing a simulated pinhole camera at any place within 3D vicinity of internal logistics. We also employed multi-view geometry among simulated cameras in order to observe trajectories more accurately. Hence, it provided us an ample opportunity of precise experimentation in order to create enormous dataset of thrown object trajectories to train an encoder-decoder bidirectional LSTM deep neural network. The trained neural network has given the best results for accurately predicting trajectory of thrown objects in real time. Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

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16 pages, 6295 KiB

Open AccessArticle

Indirect-Neural-Approximation-Based Fault-Tolerant Integrated Attitude and Position Control of Spacecraft Proximity Operations

by Fawaz W. Alsaade, Qijia Yao, Mohammed S. Al-zahrani, Ali S. Alzahrani and Hadi Jahanshahi

Sensors 2022, 22(5), 1726; https://doi.org/10.3390/s22051726 - 23 Feb 2022

Cited by 15 | Viewed by 1871

Abstract

In this paper, a neural adaptive fault-tolerant control scheme is proposed for the integrated attitude and position control of spacecraft proximity operations in the presence of unknown parameters, disturbances, and actuator faults. The proposed controller is made up of a relative attitude control [...] Read more.

In this paper, a neural adaptive fault-tolerant control scheme is proposed for the integrated attitude and position control of spacecraft proximity operations in the presence of unknown parameters, disturbances, and actuator faults. The proposed controller is made up of a relative attitude control law and a relative position control law. Both the relative attitude control law and relative position control law are designed by adopting the neural networks (NNs) to approximate the upper bound of the lumped unknowns. Benefiting from the indirect neural approximation, the proposed controller does not need any model information for feedback. In addition, only two adaptive parameters are required for the indirect neural approximation, and the online calculation burden of the proposed controller is therefore significantly reduced. Lyapunov analysis shows that the overall closed-loop system is ultimately uniformly bounded. The proposed controller can ensure the relative attitude, angular velocity, position, and velocity stabilize into the small neighborhoods around the origin. Lastly, the effectiveness and superior performance of the proposed control scheme are confirmed by a simulated example. Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

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17 pages, 3316 KiB

Open AccessArticle

Rethinking Sampled-Data Control for Unmanned Aircraft Systems

by Xinkai Zhang and Justin Bradley

Sensors 2022, 22(4), 1525; https://doi.org/10.3390/s22041525 - 16 Feb 2022

Cited by 2 | Viewed by 1475

Abstract

Unmanned aircraft systems are expected to provide both increasingly varied functionalities and outstanding application performances, utilizing the available resources. In this paper, we explore the recent advances and challenges at the intersection of real-time computing and control and show how rethinking sampling strategies [...] Read more.

Unmanned aircraft systems are expected to provide both increasingly varied functionalities and outstanding application performances, utilizing the available resources. In this paper, we explore the recent advances and challenges at the intersection of real-time computing and control and show how rethinking sampling strategies can improve performance and resource utilization. We showcase a novel design framework, cyber-physical co-regulation, which can efficiently link together computational and physical characteristics of the system, increasing robust performance and avoiding pitfalls of event-triggered sampling strategies. A comparison experiment of different sampling and control strategies was conducted and analyzed. We demonstrate that co-regulation has resource savings similar to event-triggered sampling, but maintains the robustness of traditional fixed-periodic sampling forming a compelling alternative to traditional vehicle control design. Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

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22 pages, 2923 KiB

Open AccessArticle

Nonlinear Intelligent Control of Two Link Robot Arm by Considering Human Voluntary Components

by Mingcong Deng, Shotaro Kubota and Yuanhong Xu

Sensors 2022, 22(4), 1424; https://doi.org/10.3390/s22041424 - 12 Feb 2022

Cited by 4 | Viewed by 1796

Abstract

This paper proposes a nonlinear intelligent control of a two link robot arm by considering human voluntary components. In general, human arm viscoelastic properties are regulated in different manners according to various task requirements. The viscoelasticity consists of joint stiffness and viscosity. The [...] Read more.

This paper proposes a nonlinear intelligent control of a two link robot arm by considering human voluntary components. In general, human arm viscoelastic properties are regulated in different manners according to various task requirements. The viscoelasticity consists of joint stiffness and viscosity. The research of the viscoelasticity can improve the development of industrial robots, rehabilitation and sports etc. So far, some results have been shown using filtered human arm viscoelasticity measurements. That is, human motor command is removed. As a result, the dynamics of human voluntary component during movements is omitted. In this paper, based on the feedforward characteristics of human multi joint arm, a model is obtained by considering human voluntary components using a support vector regression technique. By employing the learned model, a nonlinear intelligent control of two link robot arm is proposed. Experimental results confirm the effectiveness of this proposal. Full article

(This article belongs to the Special Issue Advanced Intelligent Control in Robots)

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