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multi-Objective Loosely Synchronized Search for multi-Objective multi-agent path finding with Asynchronous Actions

Journal of Shanghai Jiaotong university(Science) 2024年第4期29卷 667-677页

作者： DU Haikuo GUO Zhengyu ZHANG Lulu CAI Yunze Department of Automation Shanghai Jiao Tong UniversityShanghai200240China Key Laboratory of System Control and Information Processing Ministry of EducationShanghai200240China China Airborne Missile Academy LuoyangHenan471000China National Key Laboratory of Air-based Information Perception and Fusion LuoyangHenan471000China

In recent years,the path planning for multi-agent technology has gradually matured,and has made breakthrough *** main difficulties in path planning for multi-agent are large state space,long algorithm running time,multiple optimization objectives,and asynchronous action of multiple *** solve the above problems,this paper first introduces the main problem of the research:multi-objective multi-agent path finding with asynchronous action,and proposes the algorithm framework of multi-objective loose synchronous(MO-LS)*** combining A*and M*,MO-LS-A*and MO-LS-M*algorithms are respectively *** completeness and optimality of the algorithm are proved,and a series of comparative experiments are designed to analyze the factors affecting the performance of the algorithm,verifying that the proposed MO-LS-M*algorithm has certain advantages.

关键词： multi-agent path finding multi-objective path planning asynchronous action loosely synchronous search

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multi-agent path finding Method Based on Evolutionary Reinforcement Learning

Multi-Agent Path Finding Method Based on Evolutionary Reinfo...

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第43届中国控制会议

作者： Qinru Shi Meiqin Liu Senlin Zhang Ronghao Zheng Xuguang Lan College of Electrical Engineering Zhejiang University

The multi-agent path finding(MAPF) problem is crucial to improve the efficiency of warehouse systems. Compared with traditional centralized methods, which encounter escalating computational complexities with increasing scale,reinforcement learning-based methods has been proven to be an effective method for solving MAPF problem. Nevertheless, in the complex and large-scale scenarios, the policies learned by existing reinforcement learning-based methods are generally inadequate to address the challenges effectively. By leveraging the concepts of policy evaluation and policy evolution, this paper aims to improve performance and sample efficiency. Consequently, we introduce an MAPF method based on evolutionary reinforcement learning. In particular, we design a collaborative policy network model based on reinforcement ***, a novel evolutionary reinforcement learning training framework is constructed. Through the quantitative evaluation mechanism, policy evaluation is carried out, and evolutionary algorithm is used for policy evolution, so that the collaborative policy could better guide the agent to complete the path finding task. We test on high-density warehouse environment instances of various map sizes, and the experimental results show that our method has high success rate and low average steps.

关键词： multi-agent systems multi-agent path finding reinforcement learning evolutionary algorithm deep learning in robotics and automation

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Development of multi-agent-Based Indoor 3D Reconstruction

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Computers, Materials & Continua 2024年第10期81卷 161-181页

作者： Hoi Chuen Cheng Frederick Ziyang Hong Babar Hussain Yiru Wang Chik Patrick Yue Optical Wireless Lab Department of Electronic and Computer EngineeringThe Hong Kong University of Science and TechnologyHong KongChina

Large-scale indoor 3D reconstruction with multiple robots faces challenges in core enabling *** work contributes to a framework addressing localization,coordination,and vision processing for multi-agent reconstruction.A system architecture fusing visible light positioning,multi-agent path finding via reinforcement learning,and 360°camera techniques for 3D reconstruction is *** visible light positioning algorithm leverages existing lighting for centimeter-level localization without additional ***,a decentralized reinforcement learning approach is developed to solve the multi-agent path finding problem,with communications among agents *** 3D reconstruction pipeline utilizes equirectangular projection from 360°cameras to facilitate depth-independent reconstruction from posed monocular images using neural *** validation demonstrates centimeter-level indoor navigation and 3D scene reconstruction capabilities of our *** challenges and limitations stemming from the above enabling technologies are discussed at the end of each corresponding *** summary,this research advances fundamental techniques for multi-robot indoor 3D modeling,contributing to automated,data-driven applications through coordinated robot navigation,perception,and modeling.

关键词： multi-agent system multi-robot human collaboration visible light communication visible light positioning 3D reconstruction reinforcement learning multi-agent path finding

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Effective Decoupled Planning for Continuous multi-agent Pickup and Delivery

Effective Decoupled Planning for Continuous Multi-Agent Pick...

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第32届中国控制与决策会议

作者： Zhenbang Nie Peng Zeng Haibin Yu State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences University of Chinese Academy of Sciences

Continuous MAPD（multi-agent Pickup and Delivery） problem is widely encountered in material transportation ***（Token Passing） is a conflict-free,deadlock-free algorithm for continuous MAPD ***,when TP is used,the number of tasks simultaneously handled by agents is limited by the number of task endpoint *** solve this problem,ITP（Improved Token Passing） is proposed in this *** extra path planning procedure is introduced to improve agent utilization.A complete and optimal A*-based algorithm is designed to solve the low level single agent path planning *** increases system throughput by exploiting all available agents,and remains conflict-free and *** result shows that ITP outperforms TP in terms of system makespan under heavy workload in congested workspace.

关键词： multi-agent Pickup and Delivery multi-agent path finding Decoupled path Planning

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