首页|Mlti-Robot Task Allocation Using Multimodal Multi-Objective Evolutionary Algorithm Based on Deep Reinforcement Learning

Mlti-Robot Task Allocation Using Multimodal Multi-Objective Evolutionary Algorithm Based on Deep Reinforcement Learning

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The overall performance of multi-robot collaborative systems is significantly affected by the multi-robot task allocation.To improve the effectiveness,robustness,and safety of multi-robot collaborative systems,a multimodal multi-objective evolutionary algorithm based on deep reinforcement learning is proposed in this paper.The improved multimodal multi-objective evolutionary algorithm is used to solve multi-robot task allo-cation problems.Moreover,a deep reinforcement learning strategy is used in the last generation to provide a high-quality path for each assigned robot via an end-to-end manner.Comparisons with three popular multimodal multi-objective evolutionary algorithms on three different scenarios of multi-robot task allocation problems are carried out to verify the performance of the proposed algorithm.The experimental test results show that the proposed algorithm can generate sufficient equivalent schemes to improve the availability and robustness of multi-robot collaborative systems in uncertain environments,and also produce the best scheme to improve the overall task execution efficiency of multi-robot collaborative systems.

multi-robot task allocationmulti-robot cooperationpath planningmultimodal multi-objective evo-lutionary algorithmdeep reinforcement learning

苗镇华、黄文焘、张依恋、范勤勤

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Logistics Research Center,Shanghai Maritime University,Shanghai 201306,China

Key Laboratory of Control of Power Transmission and Conversion of Ministry of Education,Shanghai Jiao Tong University,Shanghai 200240,China

Key Laboratory of Marine Technology and Control Engineering of Ministry of Communications,Shanghai Maritime University,Shanghai 201306,China

Shanghai Pujiang ProgramNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of China-Shandong Joint Fund

22PJD0306160324471904116U2006228

2024

10.1007/s12204-023-2679-7

上海交通大学学报(英文版)
上海交通大学

上海交通大学学报(英文版)

影响因子：0.151
ISSN：1007-1172
年,卷(期)：2024.29(3)