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

• Published in: Shanghai jiao tong da xue xue bao Vol. 29; no. 3; pp. 377 - 387
• Main Authors: Miao, Zhenhua, Huang, Wentao, Zhang, Yilian, Fan, Qinqin
• Format: Journal Article
• Language: English
• Published: Shanghai Shanghai Jiaotong University Press 2024; Springer Nature B.V
• Subjects: Algorithms; Architecture; Collaboration; Computer Science; Deep learning; Electrical Engineering; Engineering; Evolutionary algorithms; Genetic algorithms; Learning; Life Sciences; Machine learning; Materials Science; Multiple robots; Reinforcement; Robots; Robustness; System effectiveness; A; 多机器人协同; TP301.6; multi-robot cooperation; multimodal multi-objective evolutionary algorithm; 深度强化学习; multi-robot task allocation; 多模态多目标进化算法; path planning; deep reinforcement learning; 多机器人任务分配; 路径规划
• ISSN: 1007-1172; 1674-8115; 1995-8188
• DOI: 10.1007/s12204-023-2679-7

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 allocation 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 multirobot collaborative systems in uncertain environments, and also produce the best scheme to improve the overall task execution efficiency of multi-robot collaborative systems.