J Shanghai Jiaotong Univ Sci ›› 2024, Vol. 29 ›› Issue (3): 377-387.doi: 10.1007/s12204-023-2679-7
所属专题: 智能机器人
• • 下一篇
苗镇华1,黄文焘2,张依恋3,范勤勤1*
接受日期:
2023-08-24
出版日期:
2024-05-28
发布日期:
2024-05-28
MIAO Zhenhua1(苗镇华),HUANG Wentao2(黄文焘),ZHANG Yilian3(张依恋), FAN Qinqin1*(范勤勤)
Accepted:
2023-08-24
Online:
2024-05-28
Published:
2024-05-28
摘要: 多机器人任务分配直接影响多机器人协作系统的整体性能。为提高多机器人协作系统的有效性、鲁棒性和安全性,本文提出一种基于深度强化学习的多模态多目标进化算法。在所提算法中,使用一种改进的多模态多目标进化算法来对多机器人任务分配问题进行求解,并在最后一代利用深度强化学习以端到端的方式给出各个机器人执行任务的路线。为验证所提算法的性能,与三种知名的多模态多目标进化算法在三种不同场景的多机器人任务分配问题上进行比较。实验结果表明,所提算法能够提供尽可能多的等效方案来提高多机器人协作系统在不确定环境下的可用性和鲁棒性,并且能够找到最佳方案来提高多机器人协作系统的整体任务执行效率。
中图分类号:
苗镇华1, 黄文焘2, 张依恋3, 范勤勤1. 基于深度强化学习的多模态多目标多机器人任务分配算法[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(3): 377-387.
MIAO Zhenhua(苗镇华), HUANG Wentao(黄文焘), ZHANG Yilian(张依恋), FAN Qinqin(范勤勤). Multi-Robot Task Allocation Using Multimodal Multi-Objective Evolutionary Algorithm Based on Deep Reinforcement Learning[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(3): 377-387.
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