Coordination and Communication of Multi-Robot Systems

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

Deadline for manuscript submissions: 15 August 2025 | Viewed by 287

Special Issue Editor


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Guest Editor
Department of Information Technology, Kennesaw State University (KSU), Kennesaw, GA 30144, USA
Interests: automated and intelligent robotic systems for logistics that involve machine learning; autonomous robotics; indoor wireless localization; radio-frequency identification (RFID) applications

Special Issue Information

Dear Colleagues,

As robotics continues to advance, multi-robot systems are playing an increasingly pivotal role in various fields, including manufacturing, agriculture, healthcare, and disaster response. The effectiveness of such systems relies heavily on robust coordination and communication strategies that allow robots to work seamlessly in dynamic and uncertain environments. This research area has the potential to unlock groundbreaking applications, transforming industries and addressing global challenges. We invite you to contribute to this Special Issue, which will explore state-of-the-art approaches and technologies that enhance the autonomy, efficiency, and reliability of multi-robot systems.

This Special Issue will provide a platform for researchers to share innovative solutions and insights that advance the field of multi-robot coordination and communication. By addressing challenges such as distributed decision-making, fault tolerance, adaptive communication protocols, and task allocation, this Special Issue aligns with the journal’s mission of promoting excellence in robotics and intelligent systems research.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Distributed coordination and control mechanisms;
  • Communication protocols for multi-robot systems;
  • Swarm robotics and emergent behaviors;
  • Multi-agent reinforcement learning applications;
  • Human-robot interaction in multi-robot contexts;
  • Fault-tolerant and resilient system designs;
  • Task allocation and optimization strategies;
  • Scalable and adaptive robotic systems;
  • Real-world multi-robot system deployments and case studies;
  • Integrated sensing and communication (ISAC) in robots.

I look forward to receiving your contributions.

Dr. Jian Zhang
Guest Editor

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Keywords

  • multi-robot coordination
  • communication protocols
  • swarm robotics
  • distributed systems
  • fault tolerance
  • multi-agent reinforcement learning
  • human–robot interaction
  • task allocation
  • adaptive robotics
  • dynamic environments

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Published Papers (1 paper)

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Research

18 pages, 956 KiB  
Article
A Modular Prescribed Performance Formation Control Scheme of a High-Order Multi-Agent System with a Finite-Time Extended State Observer
by Zhihan Shi, Weisong Han, Chen Zhang and Guangming Zhang
Electronics 2025, 14(9), 1783; https://doi.org/10.3390/electronics14091783 - 27 Apr 2025
Viewed by 127
Abstract
This paper proposes a modular control framework for high-order nonlinear multi-agent systems (MASs) to achieve distributed finite-time formation tracking with a prescribed performance. The design integrates two modules to address uncertainties and safety constraints simultaneously. Module I—Prescribed Performance-Based Trajectory Generation: A virtual signal [...] Read more.
This paper proposes a modular control framework for high-order nonlinear multi-agent systems (MASs) to achieve distributed finite-time formation tracking with a prescribed performance. The design integrates two modules to address uncertainties and safety constraints simultaneously. Module I—Prescribed Performance-Based Trajectory Generation: A virtual signal generator constructs collision/connectivity-aware reference trajectories by encoding time-varying performance bounds into formation errors. It ensures network rigidity and optimal formation convergence through dynamic error transformation. Module II—Anti-disturbance Tracking Control: A finite-time extended state observer (FTESO) estimates and compensates for uncertainties within a finite time, while a time-varying surface controller drives tracking errors into predefined performance funnels. This module guarantees rapid error convergence without violating the transient constraints from Module I. The simulations verified the accelerated formation reconfiguration under disturbances, and thus, demonstrated improved robustness and convergence over asymptotic approaches. The framework offers a systematic solution for safety-critical MAS coordination with heterogeneous high-order dynamics. Full article
(This article belongs to the Special Issue Coordination and Communication of Multi-Robot Systems)
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