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Design, Dynamics and Control of Robots

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 22191

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Special Issue Editors

Dr. Zhan Li

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Guest Editor

School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
Interests: motion control; industrial robot control; robust control of small UAVs; cooperative control of multi-vehicle systems

Dr. Zhang Chen

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Guest Editor

Department of Automation, Tsinghua University, Beijing 100086, China
Interests: robot control; teleoperation and autonomous robot systems

Dr. Yiyong Sun

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Guest Editor

School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: robotics; safe and reliable intelligent control; robot compliant control

Special Issue Information

Dear Colleagues,

Robots are getting increasingly complex in order to achieve difficult operations, which cooperate with or substitute human operators to perform a growing variety of tasks. Robot systems are often designed with comprehensive utilization of sensors, vision modules, actuators, controllers, etc. Therefore, the research on the design, dynamics, and control of various types of robots is of great importance to achieve better performance on different tasks.

Nowadays, robot tasks in a wide range of fields require intelligent and flexible actions in unstructured/fast-changing working environments, which brought great challenges to the decision, planning and control of robot systems. On the other hand, a deeper task-specific understanding of the design and dynamics of robot systems is essential to achieve superior task performance both on low-level control and higher lever learning-based decision/planning. Therefore, it is expected that various robot systems could be greatly improved with advances in design, dynamics, and control methods. In addition, the challenges brought by more complex robot applications could also push the development of more powerful control methods.

In an effort to disseminate current design, dynamics, and control advances for complex robot applications, a Special Issue in this area is proposed, which will provide a platform for scientists, engineers and industrial practitioners to present their latest theoretical and technological advancements in the design/development of complex robot systems, task-specific methods for robot control, modeling, planning, perception, decision, and various related applications of these techniques.

The topics of this Special Issue include, but are not limited to the following areas:

Design, and development of robot systems/parts for task-specific applications;
Dynamics modeling, planning, and parameter identification for complex robots;
Robot optimal control, adaptive control, intelligent control and system optimization;
Robot perception, recognition, guidance, navigation, mapping and localization;
Learning-based robot decision, cooperation, environments and situation understanding;
Networked control for robots;
Robot design for fast, reliable and low-cost intelligent computation and engineering applications.

Dr. Zhan Li
Dr. Zhang Chen
Dr. Yiyong Sun
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Keywords

robotics
dynamics modeling and control
intelligent systems
robot design
robot control

Published Papers (12 papers)

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Research

23 pages, 3182 KiB

Open AccessArticle

Collision Avoidance Second Order Sliding Mode Control of Satellite Formation with Air-Floated Platform Semi-Physical Simulation

by Ji Zhang, Yili Wang, Jun Jia, Chuanguo Chi and Huayi Li

Electronics 2023, 12(14), 3179; https://doi.org/10.3390/electronics12143179 - 21 Jul 2023

Cited by 1 | Viewed by 919

Abstract

As the number of satellites in orbit increases, the issue of flight safety in spacecraft formation orbit control has become increasingly prominent. With this in mind, this paper designs a second-order terminal sliding mode controller for spacecraft formation obstacle avoidance based on an artificial potential function (APF). To demonstrate the effectiveness of the controller, this paper first constructs a Lyapunov function to prove its stability and then verifies its theoretical validity through numerical simulation. Finally, a satellite simulator is used for semi-physical simulation to verify the practical effectiveness of the controller proposed in this paper. Full article

(This article belongs to the Special Issue Design, Dynamics and Control of Robots)

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14 pages, 798 KiB

Open AccessArticle

Sign Language Gesture Recognition and Classification Based on Event Camera with Spiking Neural Networks

by Xuena Chen, Li Su, Jinxiu Zhao, Keni Qiu, Na Jiang and Guang Zhai

Electronics 2023, 12(4), 786; https://doi.org/10.3390/electronics12040786 - 04 Feb 2023

Cited by 8 | Viewed by 3982

Abstract

Sign language recognition has been utilized in human–machine interactions, improving the lives of people with speech impairments or who rely on nonverbal instructions. Thanks to its higher temporal resolution, less visual redundancy information and lower energy consumption, the use of an event camera with a new dynamic vision sensor (DVS) shows promise with regard to sign language recognition with robot perception and intelligent control. Although previous work has focused on event camera-based, simple gesture datasets, such as DVS128Gesture, event camera gesture datasets inspired by sign language are critical, which poses a great impediment to the development of event camera-based sign language recognition. An effective method to extract spatio-temporal features from event data is significantly desired. Firstly, the event-based sign language gesture datasets are proposed and the data have two sources: traditional sign language videos to event stream (DVS_Sign_v2e) and DAVIS346 (DVS_Sign). In the present dataset, data are divided into five classification, verbs, quantifiers, position, things and people, adapting to actual scenarios where robots provide instruction or assistance. Sign language classification is demonstrated in spike neuron networks with a spatio-temporal back-propagation training method, leading to the best recognition accuracy of 77%. This work paves the way for the combination of event camera-based sign language gesture recognition and robotic perception for the future intelligent systems. Full article

(This article belongs to the Special Issue Design, Dynamics and Control of Robots)

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18 pages, 9464 KiB

Open AccessArticle

Model-Based Coordinated Trajectory Tracking Control of Skid-Steer Mobile Robot with Timing-Belt Servo System

by Lunfei Liang, Houde Liu, Xinliang Li, Xiaojun Zhu, Bin Lan, Yu Liu and Xueqian Wang

Electronics 2023, 12(3), 699; https://doi.org/10.3390/electronics12030699 - 31 Jan 2023

Cited by 4 | Viewed by 1913

Abstract

Four-wheel, independently driven skid-steer mobile robots have been widely used in some fields, such as indoor product shipping and outdoor inspection and exploration. Traditional skid-steer mobile robot controllers often use a kinematics controller to obtain the desired speed of each wheel, complete speed closed-loop control of each wheel and achieve the robot’s trajectory tracking control. However, the controller based on kinematics may lead to robot chattering and wheel spin from being directly driven by the motor on uneven grounds. To solve these problems, we developed a four-wheel, independently driven skid-steer mobile robot with a damping module for the timing-belt servo system and proposed a model-based coordinated trajectory tracking control method with the timing-belt servo system. First, the kinematics and dynamics of the mobile robot are established, including the chassis kinematics and dynamics, as well as the dynamics of the timing-belt servo system. Secondly, the hierarchical control law is designed, which has adaptive robust control of the upper-level robot chassis, middle-level control allocation approach, and adaptive robust control of the bottom-level timing-belt servo system. Finally, the proposed method is verified by the robot’s trajectory tracking control comparison simulation experiments and has a better control performance. Full article

(This article belongs to the Special Issue Design, Dynamics and Control of Robots)

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13 pages, 7606 KiB

Open AccessArticle

Neural Adaptive Impedance Control for Force Tracking in Uncertain Environment

by Hao An, Chao Ye, Zikang Yin and Weiyang Lin

Electronics 2023, 12(3), 640; https://doi.org/10.3390/electronics12030640 - 27 Jan 2023

Cited by 1 | Viewed by 1731

Abstract

Torque-based impedance control, a kind of classical active compliant control, is widely required in human–robot interaction, medical rehabilitation, and other fields. Adaptive impedance control effectively tracks the force when the robot comes in contact with an unknown environment. Conventional adaptive impedance control (AIC) introduces the force tracking error of the last moment to adjust the controller parameters online, which is an indirect method. In this paper, joint friction in the robot system is first identified and compensated for to enable the excellent performance of torque-based impedance control. Second, neural networks are inserted into the torque-based impedance controller, and a neural adaptive impedance control (NAIC) scheme with directly online optimized parameters is proposed. In addition, NAIC can be deployed directly without the need for data collection and training. Simulation studies and real-world experiments with a six link rotary robot manipulator demonstrate the excellent performance of NAIC. Full article

(This article belongs to the Special Issue Design, Dynamics and Control of Robots)

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14 pages, 3458 KiB

Open AccessArticle

Innovation-Superposed Simultaneous Localization and Mapping of Mobile Robots Based on Limited Augmentation

by Liu Yang, Chunhui Li, Wenlong Song and Zhan Li

Electronics 2023, 12(3), 587; https://doi.org/10.3390/electronics12030587 - 24 Jan 2023

Cited by 2 | Viewed by 1198

Abstract

In this paper, Aaiming at the problem of simultaneous localization mapping (SLAM) for mobile robots, a limited-augmentation innovation superposition (LAIS) is proposed to solve the problems occurring in SLAM. By extending the single-time innovation superposition to multi-time innovation, the error accumulation during the movement of mobile robots is reduced and the accuracy of the algorithm is improved. At the same time, when the number of feature points observed by the sensor exceeds the threshold, the sensor range is restricted. Therefore, only the qualified feature points are added to the system state vector, which reduces the calculation amount of the algorithm and improves the running speed. Simulation results show that compared with other algorithms, LAIS has higher accuracy and higher running speed in environmental maps with a different number of landmark points. Full article

(This article belongs to the Special Issue Design, Dynamics and Control of Robots)

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18 pages, 3882 KiB

Open AccessArticle

Robot Manipulation Skills Transfer for Sim-to-Real in Unstructured Environments

by Zikang Yin, Chao Ye, Hao An, Weiyang Lin and Zhifeng Wang

Electronics 2023, 12(2), 411; https://doi.org/10.3390/electronics12020411 - 13 Jan 2023

Viewed by 1469

Abstract

Robot force control that needs to be customized for the robot structure in unstructured environments with difficult-to-tune parameters guarantees robots’ compliance and safe human–robot interaction in an increasingly expanding work environment. Although reinforcement learning provides a new idea for the adaptive adjustment of these parameters, the policy often needs to be trained from scratch when used in new robotics, even in the same task. This paper proposes the episodic Natural Actor-Critic algorithm with action limits to improve robot admittance control and transfer motor skills between robots. The motion skills learned by simple simulated robots can be applied to complex real robots, reducing the difficulty of training and time consumption. The admittance control ensures the realizability and mobility of the robot’s compliance in all directions. At the same time, the reinforcement learning algorithm builds up the environment model and realizes the adaptive adjustment of the impedance parameters during the robot’s movement. In typical robot contact tasks, motor skills are trained in a robot with a simple structure in simulation and used for a robot with a complex structure in reality to perform the same task. The real robot’s performance in each task is similar to the simulated robot’s in the same environment, which verifies the method’s effectiveness. Full article

(This article belongs to the Special Issue Design, Dynamics and Control of Robots)

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13 pages, 4036 KiB

Open AccessArticle

The Study of Crash-Tolerant, Multi-Agent Offensive and Defensive Games Using Deep Reinforcement Learning

by Xilun Li, Zhan Li, Xiaolong Zheng, Xuebo Yang and Xinghu Yu

Electronics 2023, 12(2), 327; https://doi.org/10.3390/electronics12020327 - 08 Jan 2023

Cited by 3 | Viewed by 1671

Abstract

In the multi-agent offensive and defensive game (ODG), each agent achieves its goal by cooperating or competing with other agents. The multi-agent deep reinforcement learning (MADRL) method is applied in similar scenarios to help agents make decisions. In various situations, the agents of both sides may crash due to collisions. However, the existing algorithms cannot deal with the situation where the number of agents reduces. Based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm, we study a method to deal with a reduction in the number of agents in the training process without changing the structure of the neural network (NN), which is called the frozen agent method for the MADDPG (FA-MADDPG) algorithm. In addition, we design a distance–collision reward function to help agents learn strategies better. Through the experiments in four scenarios with different numbers of agents, it is verified that the algorithm we proposed can not only successfully deal with the problem of agent number reduction in the training stage but also show better performance and higher efficiency than the MADDPG algorithm in simulation. Full article

(This article belongs to the Special Issue Design, Dynamics and Control of Robots)

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17 pages, 2897 KiB

Open AccessArticle

Spiking Neural-Networks-Based Data-Driven Control

by Yuxiang Liu and Wei Pan

Electronics 2023, 12(2), 310; https://doi.org/10.3390/electronics12020310 - 07 Jan 2023

Cited by 1 | Viewed by 2336

Abstract

Machine learning can be effectively applied in control loops to make optimal control decisions robustly. There is increasing interest in using spiking neural networks (SNNs) as the apparatus for machine learning in control engineering because SNNs can potentially offer high energy efficiency, and new SNN-enabling neuromorphic hardware is being rapidly developed. A defining characteristic of control problems is that environmental reactions and delayed rewards must be considered. Although reinforcement learning (RL) provides the fundamental mechanisms to address such problems, implementing these mechanisms in SNN learning has been underexplored. Previously, spike-timing-dependent plasticity learning schemes (STDP) modulated by factors of temporal difference (TD-STDP) or reward (R-STDP) have been proposed for RL with SNN. Here, we designed and implemented an SNN controller to explore and compare these two schemes by considering cart-pole balancing as a representative example. Although the TD-based learning rules are very general, the resulting model exhibits rather slow convergence, producing noisy and imperfect results even after prolonged training. We show that by integrating the understanding of the dynamics of the environment into the reward function of R-STDP, a robust SNN-based controller can be learned much more efficiently than TD-STDP. Full article

(This article belongs to the Special Issue Design, Dynamics and Control of Robots)

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16 pages, 1669 KiB

Open AccessArticle

VW-SC3D: A Sparse 3D CNN-Based Spatial–Temporal Network with View Weighting for Skeleton-Based Action Recognition

by Xiaotian Lin, Leiyang Xu, Songlin Zhuang and Qiang Wang

Electronics 2023, 12(1), 117; https://doi.org/10.3390/electronics12010117 - 27 Dec 2022

Viewed by 1287

Abstract

In recent years, human action recognition has received increasing attention as a significant function of human–machine interaction. The human skeleton is one of the most effective representations of human actions because it is highly compact and informative. Many recent skeleton-based action recognition methods are based on graph convolutional networks (GCNs) as they preserve the topology of the human skeleton while extracting features. Although many of these methods give impressive results, there are some limitations in robustness, interoperability, and scalability. Furthermore, most of these methods ignore the underlying information of view direction and rely on the model to learn how to adjust the view from training data. In this work, we propose VW-SC3D, a spatial–temporal model with view weighting for skeleton-based action recognition. In brief, our model uses a sparse 3D CNN to extract spatial features for each frame and uses a transformer encoder to obtain temporal information within the frames. Compared to GCN-based methods, our method performs better in extracting spatial–temporal features and is more adaptive to different types of 3D skeleton data. The sparse 3D CNN makes our model more computationally efficient and more flexible. In addition, a learnable view weighting module enhances the robustness of the proposed model against viewpoint changes. A test on two different types of datasets shows a competitive result with SOTA methods, and the performance is even better in view-changing situations. Full article

(This article belongs to the Special Issue Design, Dynamics and Control of Robots)

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21 pages, 1109 KiB

Open AccessArticle

Distributed Adaptive NN-Based Attitude Synchronous Tracking Control with Input Saturation

by Zhenyu Feng, Jiawei Wang, Neng Wan and Huayi Li

Electronics 2022, 11(24), 4093; https://doi.org/10.3390/electronics11244093 - 08 Dec 2022

Cited by 1 | Viewed by 843

Abstract

The attitude synchronization tracking problem for spacecraft formation flying is investigated in this paper based on sliding-mode control and a Chebyshev neural network (ChNN). A distributed attitude cooperative controller is designed for a group of spacecrafts to guarantee that each individual spacecraft will track the reference attitude of the virtual leader in the presence of external disturbances, system uncertainties and input saturation. An adaptive ChNN is introduced to approximate the system nonlinear uncertainties and bounded external disturbances online, and a switch function, which acts as a switching signal between the adaptive ChNN controller and the robust control law, is applied to limit the output of the ChNN approximator. Then, utilizing Nussbaum-type functions, an auxiliary control system is designed to counteract the nonlinearities caused by input saturation. Finally, a numerical simulation example is provided to illustrate the robustness and effectiveness of the proposed attitude control scheme. Full article

(This article belongs to the Special Issue Design, Dynamics and Control of Robots)

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31 pages, 6638 KiB

Open AccessArticle

Hierarchical Clustering-Based Image Retrieval for Indoor Visual Localization

by Guanyuan Feng, Zhengang Jiang, Xuezhi Tan and Feihao Cheng

Electronics 2022, 11(21), 3609; https://doi.org/10.3390/electronics11213609 - 04 Nov 2022

Cited by 6 | Viewed by 1713

Abstract

Visual localization is employed for indoor navigation and embedded in various applications, such as augmented reality and mixed reality. Image retrieval and geometrical measurement are the primary steps in visual localization, and the key to improving localization efficiency is to reduce the time consumption of the image retrieval. Therefore, a hierarchical clustering-based image-retrieval method is proposed to hierarchically organize an off-line image database, resulting in control of the time consumption of image retrieval within a reasonable range. The image database is hierarchically organized by two stages: scene-level clustering and sub-scene-level clustering. In scene-level clustering, an improved cumulative sum algorithm is proposed to detect change points and then group images by global features. On the basis of scene-level clustering, a feature tracking-based method is introduced to further group images into sub-scene-level clusters. An image retrieval algorithm with a backtracking mechanism is designed and applied for visual localization. In addition, a weighted KNN-based visual localization method is presented, and the estimated query position is solved by the Armijo–Goldstein algorithm. Experimental results indicate that the running time of image retrieval does not linearly increase with the size of image databases, which is beneficial to improving localization efficiency. Full article

(This article belongs to the Special Issue Design, Dynamics and Control of Robots)

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16 pages, 953 KiB

Open AccessFeature PaperArticle

Online Reinforcement-Learning-Based Adaptive Terminal Sliding Mode Control for Disturbed Bicycle Robots on a Curved Pavement

by Xianjin Zhu, Yang Deng, Xudong Zheng, Qingyuan Zheng, Bin Liang and Yu Liu

Electronics 2022, 11(21), 3495; https://doi.org/10.3390/electronics11213495 - 28 Oct 2022

Cited by 4 | Viewed by 1524

Abstract

The reaction wheel is able to help improve the balancing ability of a bicycle robot on curved pavement. However, preserving good control performances for such a robot that is driving on unstructured surfaces under matched and mismatched disturbances is challenging due to the underactuated characteristic and the nonlinearity of the robot. In this paper, a controller combining proximal policy optimization algorithms with terminal sliding mode controls is developed for controlling the balance of the robot. Online reinforcement-learning-based adaptive terminal sliding mode control is proposed to attenuate the influence of the matched and mismatched disturbance by adjusting parameters of the controller online. Different from several existing adaptive sliding mode approaches that only tune parameters of the reaching controller, the proposed method also considers the online adjustment of the sliding surface to provide adequate robustness against mismatched disturbances. The co-simulation experimental results in MSC Adams illustrate that the proposed controller can achieve better control performances than four existing methods for a reaction wheel bicycle robot moving on curved pavement, which verifies the robustness and applicability of the method. Full article

(This article belongs to the Special Issue Design, Dynamics and Control of Robots)

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