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23 pages, 5506 KiB

Open AccessArticle

An Adaptive Control Scheme Based on Non-Interference Nonlinearity Approximation for a Class of Nonlinear Cascaded Systems and Its Application to Flexible Joint Manipulators

by Zhangxing Liu, Hongzhe Jin and Jie Zhao

Sensors 2024, 24(10), 3178; https://doi.org/10.3390/s24103178 - 16 May 2024

Viewed by 357

Abstract

Control design for the nonlinear cascaded system is challenging due to its complicated system dynamics and system uncertainty, both of which can be considered some kind of system nonlinearity. In this paper, we propose a novel nonlinearity approximation scheme with a simplified structure, [...] Read more.

Control design for the nonlinear cascaded system is challenging due to its complicated system dynamics and system uncertainty, both of which can be considered some kind of system nonlinearity. In this paper, we propose a novel nonlinearity approximation scheme with a simplified structure, where the system nonlinearity is approximated by a steady component and an alternating component using only local tracking errors. The nonlinearity of each subsystem is estimated independently. On this basis, a model-free adaptive control for a class of nonlinear cascaded systems is proposed. A squared-error correction procedure is introduced to regulate the weight coefficients of the approximation components, which makes the whole adaptive system stable even with the unmodeled uncertainties. The effectiveness of the proposed controller is validated on a flexible joint system through numerical simulations and experiments. Simulation and experimental results show that the proposed controller can achieve better control performance than the radial basis function network control. Due to its simplicity and robustness, this method is suitable for engineering applications. Full article

(This article belongs to the Special Issue Advanced Sensing and Control Technologies for Autonomous Robots)

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25 pages, 9862 KiB

Open AccessArticle

Adhesion Coefficient Identification of Wheeled Mobile Robot under Unstructured Pavement

by Hongchao Zhang, Bao Song, Junming Xu, Hu Li and Shuhui Li

Sensors 2024, 24(4), 1316; https://doi.org/10.3390/s24041316 - 18 Feb 2024

Viewed by 595

Abstract

Because of its uneven and large slope, unstructured pavement presents a great challenge to obtaining the adhesion coefficient of pavement. An estimation method of the peak adhesion coefficient of unstructured pavement on the basis of the extended Kalman filter is proposed in this [...] Read more.

Because of its uneven and large slope, unstructured pavement presents a great challenge to obtaining the adhesion coefficient of pavement. An estimation method of the peak adhesion coefficient of unstructured pavement on the basis of the extended Kalman filter is proposed in this paper. The identification accuracy of road adhesion coefficients under unstructured pavement is improved by introducing the equivalent suspension model to optimize the calculation of vertical wheel load and modifying vehicle acceleration combined with vehicle posture data. Finally, the multi-condition simulation experiments with Carsim are conducted, the estimation accuracy of the adhesion coefficient is at least improved by 3.6%, and then the precision and effectiveness of the designed algorithm in the article are verified. Full article

(This article belongs to the Special Issue Advanced Sensing and Control Technologies for Autonomous Robots)

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22 pages, 13301 KiB

Open AccessArticle

NeoSLAM: Long-Term SLAM Using Computational Models of the Brain

by Carlos Alexandre Pontes Pizzino, Ramon Romankevicius Costa, Daniel Mitchell and Patrícia Amâncio Vargas

Sensors 2024, 24(4), 1143; https://doi.org/10.3390/s24041143 - 9 Feb 2024

Viewed by 809

Abstract

Simultaneous Localization and Mapping (SLAM) is a fundamental problem in the field of robotics, enabling autonomous robots to navigate and create maps of unknown environments. Nevertheless, the SLAM methods that use cameras face problems in maintaining accurate localization over extended periods across various [...] Read more.

Simultaneous Localization and Mapping (SLAM) is a fundamental problem in the field of robotics, enabling autonomous robots to navigate and create maps of unknown environments. Nevertheless, the SLAM methods that use cameras face problems in maintaining accurate localization over extended periods across various challenging conditions and scenarios. Following advances in neuroscience, we propose NeoSLAM, a novel long-term visual SLAM, which uses computational models of the brain to deal with this problem. Inspired by the human neocortex, NeoSLAM is based on a hierarchical temporal memory model that has the potential to identify temporal sequences of spatial patterns using sparse distributed representations. Being known to have a high representational capacity and high tolerance to noise, sparse distributed representations have several properties, enabling the development of a novel neuroscience-based loop-closure detector that allows for real-time performance, especially in resource-constrained robotic systems. The proposed method has been thoroughly evaluated in terms of environmental complexity by using a wheeled robot deployed in the field and demonstrated that the accuracy of loop-closure detection was improved compared with the traditional RatSLAM system. Full article

(This article belongs to the Special Issue Advanced Sensing and Control Technologies for Autonomous Robots)

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23 pages, 14577 KiB

Open AccessArticle

Implementation of an Artificially Empathetic Robot Swarm

by Joanna Siwek, Patryk Żywica, Przemysław Siwek, Adrian Wójcik, Witold Woch, Konrad Pierzyński and Krzysztof Dyczkowski

Sensors 2024, 24(1), 242; https://doi.org/10.3390/s24010242 - 31 Dec 2023

Viewed by 926

Abstract

This paper presents a novel framework for integrating artificial empathy into robot swarms to improve communication and cooperation. The proposed model uses fuzzy state vectors to represent the knowledge and environment of individual agents, accommodating uncertainties in the real world. By utilizing similarity [...] Read more.

This paper presents a novel framework for integrating artificial empathy into robot swarms to improve communication and cooperation. The proposed model uses fuzzy state vectors to represent the knowledge and environment of individual agents, accommodating uncertainties in the real world. By utilizing similarity measures, the model compares states, enabling empathetic reasoning for synchronized swarm behavior. The paper presents a practical application example that demonstrates the efficacy of the model in a robot swarm working toward a common goal. The evaluation methodology involves the open-source physical-based experimentation platform (OPEP), which emphasizes empirical validation in real-world scenarios. The paper proposes a transitional environment that enables automated and repeatable execution of experiments on a swarm of robots using physical devices. Full article

(This article belongs to the Special Issue Advanced Sensing and Control Technologies for Autonomous Robots)

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13 pages, 3090 KiB

Open AccessArticle

An Integrated Autonomous Dynamic Navigation Approach toward a Composite Air–Ground Risk Construction Scenario

by Da Jiang, Meijing Wang, Xiaole Chen, Hongchao Zhang, Kang Wang, Chengchi Li, Shuhui Li and Ling Du

Sensors 2024, 24(1), 221; https://doi.org/10.3390/s24010221 - 30 Dec 2023

Viewed by 626

Abstract

Unmanned transportation in construction scenarios presents a significant challenge due to the presence of complex dynamic on-ground obstacles and potential airborne falling objects. Consequently, the typical methodology for composite air–ground risk avoidance in construction scenarios holds enormous importance. In this paper, an integrated [...] Read more.

Unmanned transportation in construction scenarios presents a significant challenge due to the presence of complex dynamic on-ground obstacles and potential airborne falling objects. Consequently, the typical methodology for composite air–ground risk avoidance in construction scenarios holds enormous importance. In this paper, an integrated potential-field-based risk assessment approach is proposed to evaluate the threat severity of the environmental obstacles. Meanwhile, the self-adaptive dynamic window approach is suggested to manage the real-time motion planning solution for air–ground risks. By designing the multi-objective velocity sample window, we constrain the vehicle’s speed planning instructions within reasonable limits. Combined with a hierarchical decision-making mechanism, this approach achieves effective obstacle avoidance with multiple drive modes. Simulation results demonstrate that, in comparison with the traditional dynamic window approach, the proposed method offers enhanced stability and efficiency in risk avoidance, underlining its notable safety and effectiveness. Full article

(This article belongs to the Special Issue Advanced Sensing and Control Technologies for Autonomous Robots)

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26 pages, 12200 KiB

Open AccessArticle

TSG-SLAM: SLAM Employing Tight Coupling of Instance Segmentation and Geometric Constraints in Complex Dynamic Environments

by Yongchao Zhang, Yuanming Li and Pengzhan Chen

Sensors 2023, 23(24), 9807; https://doi.org/10.3390/s23249807 - 13 Dec 2023

Viewed by 903

Abstract

Although numerous effective Simultaneous Localization and Mapping (SLAM) systems have been developed, complex dynamic environments continue to present challenges, such as managing moving objects and enabling robots to comprehend environments. This paper focuses on a visual SLAM method specifically designed for complex dynamic [...] Read more.

Although numerous effective Simultaneous Localization and Mapping (SLAM) systems have been developed, complex dynamic environments continue to present challenges, such as managing moving objects and enabling robots to comprehend environments. This paper focuses on a visual SLAM method specifically designed for complex dynamic environments. Our approach proposes a dynamic feature removal module based on the tight coupling of instance segmentation and multi-view geometric constraints (TSG). This method seamlessly integrates semantic information with geometric constraint data, using the fundamental matrix as a connecting element. In particular, instance segmentation is performed on frames to eliminate all dynamic and potentially dynamic features, retaining only reliable static features for sequential feature matching and acquiring a dependable fundamental matrix. Subsequently, based on this matrix, true dynamic features are identified and removed by capitalizing on multi-view geometry constraints while preserving reliable static features for further tracking and mapping. An instance-level semantic map of the global scenario is constructed to enhance the perception and understanding of complex dynamic environments. The proposed method is assessed on TUM datasets and in real-world scenarios, demonstrating that TSG-SLAM exhibits superior performance in detecting and eliminating dynamic feature points and obtains good localization accuracy in dynamic environments. Full article

(This article belongs to the Special Issue Advanced Sensing and Control Technologies for Autonomous Robots)

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28 pages, 10838 KiB

Open AccessArticle

Path Planning of a Mobile Robot for a Dynamic Indoor Environment Based on an SAC-LSTM Algorithm

by Yongchao Zhang and Pengzhan Chen

Sensors 2023, 23(24), 9802; https://doi.org/10.3390/s23249802 - 13 Dec 2023

Viewed by 1348

Abstract

This paper proposes an improved Soft Actor–Critic Long Short-Term Memory (SAC-LSTM) algorithm for fast path planning of mobile robots in dynamic environments. To achieve continuous motion and better decision making by incorporating historical and current states, a long short-term memory network (LSTM) with [...] Read more.

This paper proposes an improved Soft Actor–Critic Long Short-Term Memory (SAC-LSTM) algorithm for fast path planning of mobile robots in dynamic environments. To achieve continuous motion and better decision making by incorporating historical and current states, a long short-term memory network (LSTM) with memory was integrated into the SAC algorithm. To mitigate the memory depreciation issue caused by resetting the LSTM’s hidden states to zero during training, a burn-in training method was adopted to boost the performance. Moreover, a prioritized experience replay mechanism was implemented to enhance sampling efficiency and speed up convergence. Based on the SAC-LSTM framework, a motion model for the Turtlebot3 mobile robot was established by designing the state space, action space, reward function, and overall planning process. Three simulation experiments were conducted in obstacle-free, static obstacle, and dynamic obstacle environments using the ROS platform and Gazebo9 software. The results were compared with the SAC algorithm. In all scenarios, the SAC-LSTM algorithm demonstrated a faster convergence rate and a higher path planning success rate, registering a significant 10.5 percentage point improvement in the success rate of reaching the target point in the dynamic obstacle environment. Additionally, the time taken for path planning was shorter, and the planned paths were more concise. Full article

(This article belongs to the Special Issue Advanced Sensing and Control Technologies for Autonomous Robots)

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23 pages, 3581 KiB

Open AccessArticle

A Software Platform for Quadruped Robots with Advanced Manipulation Capabilities

by Jae-Bong Yi, Shady Nasrat, Min-seong Jo and Seung-Joon Yi

Sensors 2023, 23(19), 8247; https://doi.org/10.3390/s23198247 - 4 Oct 2023

Cited by 1 | Viewed by 1121

Abstract

Recently, a diverse range of robots with various functionalities have become a part of our daily lives. However, these robots either lack an arm or have less capable arms, mainly used for gestures. Another characteristic of the robots is that they are wheeled-type [...] Read more.

Recently, a diverse range of robots with various functionalities have become a part of our daily lives. However, these robots either lack an arm or have less capable arms, mainly used for gestures. Another characteristic of the robots is that they are wheeled-type robots, restricting their operation to even surfaces. Several software platforms proposed in prior research have often focused on quadrupedal robots equipped with manipulators. However, many of these platforms lacked a comprehensive system combining perception, navigation, locomotion, and manipulation. This research introduces a software framework for clearing household objects with a quadrupedal robot. The proposed software framework utilizes the perception of the robot’s environment through sensor inputs and organizes household objects to their designated locations. The proposed framework was verified by experiments within a simulation environment resembling the conditions of the RoboCup@Home 2021-virtual competition involving variations in objects and poses, where outcomes demonstrate promising performance. Full article

(This article belongs to the Special Issue Advanced Sensing and Control Technologies for Autonomous Robots)

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23 pages, 16267 KiB

Open AccessArticle

Design and Implementation of an Integrated Control System for Omnidirectional Mobile Robots in Industrial Logistics

by Ahmed Neaz, Sunyeop Lee and Kanghyun Nam

Sensors 2023, 23(6), 3184; https://doi.org/10.3390/s23063184 - 16 Mar 2023

Cited by 2 | Viewed by 3019

Abstract

The integration of intelligent robots in industrial production processes has the potential to significantly enhance efficiency and reduce human adversity. However, for such robots to effectively operate within human environments, it is critical that they possess an adequate understanding of their surroundings and [...] Read more.

The integration of intelligent robots in industrial production processes has the potential to significantly enhance efficiency and reduce human adversity. However, for such robots to effectively operate within human environments, it is critical that they possess an adequate understanding of their surroundings and are able to navigate through narrow aisles while avoiding both stationary and moving obstacles. In this research study, an omnidirectional automotive mobile robot has been designed for the purpose of performing industrial logistics tasks within heavy traffic and dynamic environments. A control system has been developed, which incorporates both high-level and low-level algorithms, and a graphical interface has been introduced for each control system. A highly efficient micro-controller, namely myRIO, has been utilized as the low-level computer to control the motors with an appropriate level of accuracy and robustness. Additionally, a Raspberry Pi 4, in conjunction with a remote PC, has been utilized for high-level decision making, such as mapping the experimental environment, path planning, and localization, through the utilization of multiple Lidar sensors, IMU, and odometry data generated by wheel encoders. In terms of software programming, LabVIEW has been employed for the low-level computer, and the Robot Operating System (ROS) has been utilized for the design of the higher-level software architecture. The proposed techniques discussed in this paper provide a solution for the development of medium- and large-category omnidirectional mobile robots with autonomous navigation and mapping capabilities. Full article

(This article belongs to the Special Issue Advanced Sensing and Control Technologies for Autonomous Robots)

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17 pages, 6715 KiB

Open AccessArticle

Parametric Dynamic Distributed Containment Control of Continuous-Time Linear Multi-Agent Systems with Specified Convergence Speed

by Fei Yan, Siyi Feng, Xiangbiao Liu and Tao Feng

Sensors 2023, 23(5), 2696; https://doi.org/10.3390/s23052696 - 1 Mar 2023

Cited by 1 | Viewed by 1220

Abstract

This paper focuses on the distributed containment control of continuous-time linear multi-agent systems (MASs) with multiple leaders over fixed topology. A parametric dynamic compensated distributed control protocol is proposed in which both the information from the observer in the virtual layer and actual [...] Read more.

This paper focuses on the distributed containment control of continuous-time linear multi-agent systems (MASs) with multiple leaders over fixed topology. A parametric dynamic compensated distributed control protocol is proposed in which both the information from the observer in the virtual layer and actual adjacent agents are employed. The necessary and sufficient conditions of the distributed containment control are derived based on the standard linear quadratic regulator (LQR). On this basis, the dominant poles are configured by using the modified linear quadratic regulator (MLQR) optimal control and Geršgorin’s circle criterion, hence the containment control with specified convergence speed of the MAS is achieved. Another main advantage of the proposed design is, in the case of virtual layer failure, by adjusting parameters the dynamic control protocol reduces to static, and the convergence speed can still be specified through the dominant pole assignment method combined with inverse optimal control. Finally, typical numerical examples are presented to demonstrate the effectiveness of theoretical results. Full article

(This article belongs to the Special Issue Advanced Sensing and Control Technologies for Autonomous Robots)

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17 pages, 7547 KiB

Open AccessArticle

Adaptive Model Predictive Control for Mobile Robots with Localization Fluctuation Estimation

by Jie Meng, Hanbiao Xiao, Liyu Jiang, Zhaozheng Hu, Liquan Jiang and Ning Jiang

Sensors 2023, 23(5), 2501; https://doi.org/10.3390/s23052501 - 23 Feb 2023

Cited by 3 | Viewed by 1584

Abstract

Mobile robots are widely employed in various fields to perform autonomous tasks. In dynamic scenarios, localization fluctuations are unavoidable and obvious. However, common controllers do not consider the impact of localization fluctuations, resulting in violent jittering or poor trajectory tracking of the mobile [...] Read more.

Mobile robots are widely employed in various fields to perform autonomous tasks. In dynamic scenarios, localization fluctuations are unavoidable and obvious. However, common controllers do not consider the impact of localization fluctuations, resulting in violent jittering or poor trajectory tracking of the mobile robot. For this reason, this paper proposes an adaptive model predictive control (MPC) with an accurate localization fluctuation assessment for mobile robots, which balances the contradiction between precision and calculation efficiency of mobile robot control. The distinctive features of the proposed MPC are three-fold: (1) Integrating variance and entropy—a localization fluctuation estimation relying on fuzzy logic rules is proposed to enhance the accuracy of the fluctuation assessment. (2) By using the Taylor expansion-based linearization method—a modified kinematics model that considers that the external disturbance of localization fluctuation is established to satisfy the iterative solution of the MPC method and reduce the computational burden. (3) An improved MPC with an adaptive adjustment of predictive step size according to localization fluctuation is proposed, which alleviates the disadvantage of a large amount of the MPC calculation and improves the stability of the control system in dynamic scenes. Finally, verification experiments of the real-life mobile robot are offered to verify the effectiveness of the presented MPC method. Additionally, compared with PID, the tracking distance and angle error of the proposed method decrease by 74.3% and 95.3%, respectively. Full article

(This article belongs to the Special Issue Advanced Sensing and Control Technologies for Autonomous Robots)

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12 pages, 3300 KiB

Open AccessArticle

Indoor Location Technology with High Accuracy Using Simple Visual Tags

by Feng Gao and Jie Ma

Sensors 2023, 23(3), 1597; https://doi.org/10.3390/s23031597 - 1 Feb 2023

Cited by 4 | Viewed by 1481

Abstract

To achieve low-cost and robustness, an indoor location system using simple visual tags is designed by comprehensively considering accuracy and computation complexity. Only the color and shape features are used for tag detection, by which both algorithm complexity and data storage requirement are [...] Read more.

To achieve low-cost and robustness, an indoor location system using simple visual tags is designed by comprehensively considering accuracy and computation complexity. Only the color and shape features are used for tag detection, by which both algorithm complexity and data storage requirement are reduced. To manage the nonunique problem caused by the simple tag features, a fast query and matching method is further presented by using the view field of the camera and the tag azimuth. Then, based on the relationship analysis between the spatial distribution of tags and location error, a pose and position estimation method using the weighted least square algorithm is designed and works together with the interactive algorithm by the designed switching strategy. By using the techniques presented, a favorable balance is achieved between the algorithm complexity and the location accuracy. The simulation and experiment results show that the proposed method can manage the singular problem of the overdetermined equations effectively and attenuate the negative effect of unfavorable label groups. Compared with the ultrawide band technology, the location error is reduced by more than 62%. Full article

(This article belongs to the Special Issue Advanced Sensing and Control Technologies for Autonomous Robots)

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20 pages, 4958 KiB

Open AccessArticle

DCP-SLAM: Distributed Collaborative Partial Swarm SLAM for Efficient Navigation of Autonomous Robots

by Huma Mahboob, Jawad N. Yasin, Suvi Jokinen, Mohammad-Hashem Haghbayan, Juha Plosila and Muhammad Mehboob Yasin

Sensors 2023, 23(2), 1025; https://doi.org/10.3390/s23021025 - 16 Jan 2023

Cited by 5 | Viewed by 2940

Abstract

Collaborative robots represent an evolution in the field of swarm robotics that is pervasive in modern industrial undertakings from manufacturing to exploration. Though there has been much work on path planning for autonomous robots employing floor plans, energy-efficient navigation of autonomous robots in [...] Read more.

Collaborative robots represent an evolution in the field of swarm robotics that is pervasive in modern industrial undertakings from manufacturing to exploration. Though there has been much work on path planning for autonomous robots employing floor plans, energy-efficient navigation of autonomous robots in unknown environments is gaining traction. This work presents a novel methodology of low-overhead collaborative sensing, run-time mapping and localization, and navigation for robot swarms. The aim is to optimize energy consumption for the swarm as a whole rather than individual robots. An energy- and information-aware management algorithm is proposed to optimize the time and energy required for a swarm of autonomous robots to move from a launch area to the predefined destination. This is achieved by modifying the classical Partial Swarm SLAM technique, whereby sections of objects discovered by different members of the swarm are stitched together and broadcast to members of the swarm. Thus, a follower can find the shortest path to the destination while avoiding even far away obstacles in an efficient manner. The proposed algorithm reduces the energy consumption of the swarm as a whole due to the fact that the leading robots sense and discover respective optimal paths and share their discoveries with the followers. The simulation results show that the robots effectively re-optimized the previous solution while sharing necessary information within the swarm. Furthermore, the efficiency of the proposed scheme is shown via comparative results, i.e., reducing traveling distance by 13% for individual robots and up to 11% for the swarm as a whole in the performed experiments. Full article

(This article belongs to the Special Issue Advanced Sensing and Control Technologies for Autonomous Robots)

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Review

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31 pages, 1424 KiB

Open AccessReview

A Review of Sensing Technologies for Indoor Autonomous Mobile Robots

by Yu Liu, Shuting Wang, Yuanlong Xie, Tifan Xiong and Mingyuan Wu

Sensors 2024, 24(4), 1222; https://doi.org/10.3390/s24041222 - 14 Feb 2024

Cited by 1 | Viewed by 2300

Abstract

As a fundamental issue in robotics academia and industry, indoor autonomous mobile robots (AMRs) have been extensively studied. For AMRs, it is crucial to obtain information about their working environment and themselves, which can be realized through sensors and the extraction of corresponding [...] Read more.

As a fundamental issue in robotics academia and industry, indoor autonomous mobile robots (AMRs) have been extensively studied. For AMRs, it is crucial to obtain information about their working environment and themselves, which can be realized through sensors and the extraction of corresponding information from the measurements of these sensors. The application of sensing technologies can enable mobile robots to perform localization, mapping, target or obstacle recognition, and motion tasks, etc. This paper reviews sensing technologies for autonomous mobile robots in indoor scenes. The benefits and potential problems of using a single sensor in application are analyzed and compared, and the basic principles and popular algorithms used in processing these sensor data are introduced. In addition, some mainstream technologies of multi-sensor fusion are introduced. Finally, this paper discusses the future development trends in the sensing technology for autonomous mobile robots in indoor scenes, as well as the challenges in the practical application environments. Full article

(This article belongs to the Special Issue Advanced Sensing and Control Technologies for Autonomous Robots)

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