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Advances in Multi-Agent Systems: Cooperative and Intelligent Control Strategies for Complex Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 2665

Special Issue Editor


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Guest Editor
School of Mathematics and Statistics, Xidian University, Xi’an 710126, China
Interests: intelligent control, cooperative control and their applications; stability of differential equations and their applications

Special Issue Information

Dear Colleagues,

Multi-agent systems (MASs) have become a key paradigm for solving complex problems in robotics, smart grids, transportation, and industrial automation. By combining the strengths of multiple autonomous agents, MASs provide scalable, adaptive, and robust solutions. This Special Issue focuses on advancements in cooperative and intelligent control strategies for MASs, covering topics such as distributed optimization, consensus algorithms, game-theoretic frameworks, and machine learning-based control. We invite original research, reviews, and case studies that explore theoretical foundations, practical implementations, and emerging trends in MASs. The goal is to showcase innovative approaches that improve coordination, communication, and decision-making in multi-agent systems, while addressing real-world challenges. Contributions to this Special Issue will highlight the potential of MASs to transform complex applications and inspire future research directions. 

Dr. Jiaxi Chen
Guest Editor

Manuscript Submission Information

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Keywords

  • multi-agent systems
  • cooperative control
  • intelligent control
  • distributed systems
  • autonomous agents

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Published Papers (2 papers)

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Research

19 pages, 2822 KB  
Article
A New Framework for Job Shop Integrated Scheduling and Vehicle Path Planning Problem
by Ruiqi Li, Jianlin Mao, Xing Wu, Wenna Zhou, Chengze Qian and Haoshuang Du
Sensors 2026, 26(2), 543; https://doi.org/10.3390/s26020543 - 13 Jan 2026
Viewed by 595
Abstract
With the development of manufacturing industry, traditional fixed process processing methods cannot adapt to the changes in workshop operations and the demand for small batches and multiple orders. Therefore, it is necessary to introduce multiple robots to provide a more flexible production mode. [...] Read more.
With the development of manufacturing industry, traditional fixed process processing methods cannot adapt to the changes in workshop operations and the demand for small batches and multiple orders. Therefore, it is necessary to introduce multiple robots to provide a more flexible production mode. Currently, some Job Shop Scheduling Problems with Transportation (JSP-T) only consider job scheduling and vehicle task allocation, and does not focus on the problem of collision free paths between vehicles. This article proposes a novel solution framework that integrates workshop scheduling, material handling robot task allocation, and conflict free path planning between robots. With the goal of minimizing the maximum completion time (Makespan) that includes handling, this paper first establishes an extended JSP-T problem model that integrates handling time and robot paths, and provides the corresponding workshop layout map. Secondly, in the scheduling layer, an improved Deep Q-Network (DQN) method is used for dynamic scheduling to generate a feasible and optimal machining scheduling scheme. Subsequently, considering the robot’s position information, the task sequence is assigned to the robot path execution layer. Finally, at the path execution layer, the Priority Based Search (PBS) algorithm is applied to solve conflict free paths for the handling robot. The optimized solution for obtaining the maximum completion time of all jobs under the condition of conflict free path handling. The experimental results show that compared with algorithms such as PPO, the scheduling algorithm proposed in this paper has improved performance by 9.7% in Makespan, and the PBS algorithm can obtain optimized paths for multiple handling robots under conflict free conditions. The framework can handle scheduling, task allocation, and conflict-free path planning in a unified optimization process, which can adapt well to job changes and then flexible manufacturing. Full article
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18 pages, 1697 KB  
Article
Multi-Robot System for Cooperative Tidying Up with Mobile Manipulators and Transport Agents
by Jae-Bong Yi, Shady Nasrat, Dongwoon Song, Joonyoung Kim and Seung-Joon Yi
Sensors 2025, 25(11), 3269; https://doi.org/10.3390/s25113269 - 22 May 2025
Cited by 3 | Viewed by 1548
Abstract
This paper presents a system in which mobile manipulators and transport agents cooperate to solve a multi-agent pickup and delivery (MAPD) problem. The primary objective is to allocate appropriate tasks to heterogeneous robots by considering their capabilities and states. Unlike previous studies that [...] Read more.
This paper presents a system in which mobile manipulators and transport agents cooperate to solve a multi-agent pickup and delivery (MAPD) problem. The primary objective is to allocate appropriate tasks to heterogeneous robots by considering their capabilities and states. Unlike previous studies that focused on homogeneous teams or assigned distinct roles to heterogeneous robots, this work emphasizes synergy through cooperative task execution. A key feature of the proposed system is that mobile manipulators behave differently depending on whether they are paired with a transport agent. Additionally, rather than generating a full trajectory from start to end, the system plans partial trajectories, allowing dynamic re-pairing of transport agents through an auction algorithm. After re-pairing, new starting nodes are defined, and the following trajectory is updated accordingly. The proposed system is validated through simulations, and its effectiveness is demonstrated by comparing it against a baseline system without dynamic pairing. Full article
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