Recent Progress in Multi-Robot Systems

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: 15 January 2025 | Viewed by 3410

Special Issue Editors


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Guest Editor
Department of Electrical and Electronic Engineering, University of Manchester, Manchester M13 9PL, UK
Interests: distributed control; robotic path planning; multi-agent systems; distributed learning
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Guest Editor
Department of Computer Science, University College London, London WC1E 6BT, UK
Interests: model predictive control; reinforcement learning; aerial robotics; autonomous systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Engineering, University of Leicester, University Road, Leicester LE1 7RH, UK
Interests: distributed optimization; game theory; autonomous driving; control of multi-agent systems

Special Issue Information

Dear Colleagues,

Multi-robot systems have become an increasingly important area of research due to the growing demand for intelligent robots that can operate in complex environments. These systems consist of multiple robots that coordinate with each other to achieve a common goal. Recent advances in multi-robot systems have been driven by developments in sensing, communication, and control technologies, which have enabled robots to work together more efficiently and effectively. In particular, learning and control methods have played a critical role in enabling robots to operate autonomously and adaptively in dynamic and uncertain environments. This Special Issue aims to present state-of-the-art research in multi-robot systems, and to highlight the key challenges and opportunities in this field. We welcome both review papers and original research papers. Topics include, but are not limited to, the following:

  • Multi-robot cooperative control;
  • Distributed control and optimization in multi-agent systems;
  • Reinforcement learning and deep reinforcement learning for multi-robot systems;
  • Communication and sensing in multi-robot systems;
  • Multi-robot perception and sensor fusion;
  • Swarms and swarm intelligence;
  • Human-robot interaction in multi-robot systems;
  • Applications of multi-agent systems, including search and rescue, environmental monitoring, transportation, manufacturing, smart cities and energy systems, etc.

Dr. Zhongguo Li
Dr. Yunda Yan
Dr. Xuefang Wang
Guest Editors

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Keywords

  • multi-agent system
  • distributed control
  • learning-based control
  • autonomous systems
  • swarm robotics
  • cyber-physical systems

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Published Papers (2 papers)

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Research

14 pages, 2372 KiB  
Article
Dual-Arm Obstacle Avoidance Motion Planning Based on Improved RRT Algorithm
by Zhe Dong, Binrui Zhong, Jiahuan He and Zhao Gao
Machines 2024, 12(7), 472; https://doi.org/10.3390/machines12070472 - 12 Jul 2024
Viewed by 996
Abstract
This paper proposes a solution for the cooperative obstacle avoidance path planning problem in dual manipulator arms using an improved Rapidly Exploring Random Tree (RRT) algorithm. The dual manipulator arms are categorized into a main arm and a secondary arm. Initially, the obstacle [...] Read more.
This paper proposes a solution for the cooperative obstacle avoidance path planning problem in dual manipulator arms using an improved Rapidly Exploring Random Tree (RRT) algorithm. The dual manipulator arms are categorized into a main arm and a secondary arm. Initially, the obstacle avoidance path for the master arm is planned in the presence of static obstacles. Subsequently, the poses of the master arm during its movement are treated as dynamic obstacles for planning the obstacle avoidance path for the slave arm. A cost function incorporating a fast convergence policy is introduced. Additionally, adaptive weights between distance cost and variation cost are innovatively integrated into the cost function, along with increased weights for each joint, enhancing the algorithm’s effectiveness and feasibility in practical scenarios. The smoothness of the planned paths is improved through the introduction of interpolation functions. The improved algorithm is numerically modeled and simulated in MATLAB. The verification results demonstrate that the improved RRT algorithm proposed in this paper is both feasible and more efficient. Full article
(This article belongs to the Special Issue Recent Progress in Multi-Robot Systems)
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13 pages, 7600 KiB  
Communication
An Improved Super-Twisting Sliding Mode Composite Control for Quadcopter UAV Formation
by Yulong Ye, Song Hu, Xingyu Zhu and Zhenxing Sun
Machines 2024, 12(1), 32; https://doi.org/10.3390/machines12010032 - 3 Jan 2024
Cited by 1 | Viewed by 1474
Abstract
Aiming at the nonlinear and multiple disturbances in the multi-quadcopter UAV system, this paper proposes a leader–follower composite formation control strategy based on an improved super-twisted sliding mode controller (ISTSMC) and a finite-time extended state observer (FTESO). For the designed sliding mode control [...] Read more.
Aiming at the nonlinear and multiple disturbances in the multi-quadcopter UAV system, this paper proposes a leader–follower composite formation control strategy based on an improved super-twisted sliding mode controller (ISTSMC) and a finite-time extended state observer (FTESO). For the designed sliding mode control algorithm, the integral term’s switching function is replaced with a non-smooth term to reduce the vibration in the control, further improving the overall performance of the system. For external disturbances, the finite-time extended state observer achieves rapid and accurate observation of external disturbances. Finally, through formation control experiments, the reliability and superiority of the proposed composite formation controller (CFC) is validated. Full article
(This article belongs to the Special Issue Recent Progress in Multi-Robot Systems)
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