He Zichen, Dong Lu, Song Chunwei, Sun Changyin
IEEE Trans Neural Netw Learn Syst. 2023 Dec;34(12):10980-10992. doi: 10.1109/TNNLS.2022.3172168. Epub 2023 Nov 30.
In this article, a novel hybrid multirobot motion planner that can be applied under no explicit communication and local observable conditions is presented. The planner is model-free and can realize the end-to-end mapping of multirobot state and observation information to final smooth and continuous trajectories. The planner is a front-end and back-end separated architecture. The design of the front-end collaborative waypoints searching module is based on the multiagent soft actor-critic (MASAC) algorithm under the centralized training with decentralized execution (CTDE) diagram. The design of the back-end trajectory optimization module is based on the minimal snap method with safety zone constraints. This module can output the final dynamic-feasible and executable trajectories. Finally, multigroup experimental results verify the effectiveness of the proposed motion planner.
本文提出了一种新型混合多机器人运动规划器，该规划器可在无明确通信和局部可观测条件下应用。该规划器无需模型，能够实现多机器人状态和观测信息到最终平滑连续轨迹的端到端映射。该规划器采用前端和后端分离架构。前端协作航路点搜索模块基于集中训练分散执行（CTDE）框架下的多智能体软演员-评论家（MASAC）算法进行设计。后端轨迹优化模块基于带安全区约束的最小急动方法进行设计。该模块能够输出最终的动态可行且可执行的轨迹。最后，多组实验结果验证了所提出的运动规划器的有效性。
