Decentralized Motion Planning for Multi-Robot Navigation using Deep Reinforcement Learning
Decentralized Motion Planning for Multi-Robot Navigation using Deep Reinforcement Learning
复制标题
使用深度强化学习的多机器人导航的分散运动规划
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Chinmay Vilas Samak
中科院分区：
文献类型：
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作者：
Sivanathan Kandhasamy;Vinayagam Babu Kuppusamy;Tanmay Vilas Samak;Chinmay Vilas Samak
本文提出了一种基于深度强化学习的分布式运动规划框架，用于解决多机器人导航问题。为了实验研究4个相互共享有限状态信息的协作非完整机器人在3种不同环境中的导航问题，开发了一个定制的仿真器。该方法采用了分布式运动规划的概念，通过共同和共享的策略学习，允许在随机环境中对该方法进行健壮的训练和测试，因为智能体是相互独立的，并且表现出异步的运动行为。通过为智能体提供稀疏的观察空间，并要求它们生成连续的动作命令，以便高效、安全地导航到各自的目标位置，同时始终避免与其他动态同行的碰撞和静态障碍物，任务进一步加剧。实验结果以培训和部署阶段的量化措施和定性评论的形式报告。
This work presents a decentralized motion planning framework for addressing the task of multi-robot navigation using deep reinforcement learning. A custom simulator was developed in order to experimentally investigate the navigation problem of 4 cooperative non-holonomic robots sharing limited state information with each other in 3 different settings. The notion of decentralized motion planning with common and shared policy learning was adopted, which allowed robust training and testing of this approach in a stochastic environment since the agents were mutually independent and exhibited asynchronous motion behavior. The task was further aggravated by providing the agents with a sparse observation space and requiring them to generate continuous action commands so as to efficiently, yet safely navigate to their respective goal locations, while avoiding collisions with other dynamic peers and static obstacles at all times. The experimental results are reported in terms of quantitative measures and qualitative remarks for both training and deployment phases.