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Electronics
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Coordination and Communication of Multi-Robot Systems
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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 March 2026 | Viewed by 1525

Special Issue Editor

Dr. Jian Zhang

E-Mail Website
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

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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 (2 papers)

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20 pages, 794 KB  
Article
Replay-Based Domain Incremental Learning for Cross-User Gesture Recognition in Robot Task Allocation
by Kanchon Kanti Podder, Pritom Dutta and Jian Zhang
Electronics 2025, 14(19), 3946; https://doi.org/10.3390/electronics14193946 - 6 Oct 2025
Viewed by 377
Abstract
Reliable gesture interfaces are essential for coordinating distributed robot teams in the field. However, models trained in a single domain often perform poorly when confronted with new users, different sensors, or unfamiliar environments. To address this challenge, we propose a memory-efficient replay-based domain [...] Read more.
Reliable gesture interfaces are essential for coordinating distributed robot teams in the field. However, models trained in a single domain often perform poorly when confronted with new users, different sensors, or unfamiliar environments. To address this challenge, we propose a memory-efficient replay-based domain incremental learning (DIL) framework, ReDIaL, that adapts to sequential domain shifts while minimizing catastrophic forgetting. Our approach employs a frozen encoder to create a stable latent space and a clustering-based exemplar replay strategy to retain compact, representative samples from prior domains under strict memory constraints. We evaluate the framework on a multi-domain air-marshalling gesture recognition task, where an in-house dataset serves as the initial training domain and the NATOPS dataset provides 20 cross-user domains for sequential adaptation. During each adaptation step, training data from the current NATOPS subject is interleaved with stored exemplars to retain prior knowledge while accommodating new knowledge variability. Across 21 sequential domains, our approach attains 97.34% accuracy on the domain incremental setting, exceeding pooled fine-tuning (91.87%), incremental fine-tuning (80.92%), and Experience Replay (94.20%) by +5.47, +16.42, and +3.14 percentage points, respectively. Performance also approaches the joint-training upper bound (98.18%), which represents the ideal case where data from all domains are available simultaneously. These results demonstrate that memory-efficient latent exemplar replay provides both strong adaptation and robust retention, enabling practical and trustworthy gesture-based human–robot interaction in dynamic real-world deployments. Full article
(This article belongs to the Special Issue Coordination and Communication of Multi-Robot Systems)
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18 pages, 956 KB  
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 785
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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