基于行为的协同导航的安全多智能体强化学习
Safe Multi-Agent Reinforcement Learning for Behavior-Based Cooperative Navigation
AI总结：
提出融合MPC安全滤波的MARL框架，仅以编队质心目标实现移动机器人安全协同导航，零碰撞、收敛更快且可早期实机部署。
摘要：
本文研究利用安全多智能体强化学习（MARL）实现移动机器人基于行为的协同导航问题。我们的工作首次聚焦于不为机器人设置个体参考目标、而仅为编队质心设置单一目标的协同导航。这消除了使用多个路径规划器控制机器人群组所带来的复杂性。为确保安全性，我们的MARL框架采用模型预测控制（MPC），以在训练和执行过程中阻止可能导致碰撞的动作。我们在仿真和真实机器人上验证了方法的有效性，实现了不使用个体参考目标的安全基于行为协同导航，碰撞次数为零，并且与基线相比能够更快到达目标。最后，我们研究了MPC安全滤波器对学习过程的影响，结果表明该方法在训练中收敛更快，并证明即使在训练的早期阶段，我们的方法也能够安全部署到真实机器人上。
英文摘要：
In this paper, we address the problem of behavior-based cooperative navigation of mobile robots using safe multi-agent reinforcement learning~(MARL). Our work is the first to focus on cooperative navigation without individual reference targets for the robots, using a single target for the formation's centroid. This eliminates the complexities involved in having several path planners to control a team of robots. To ensure safety, our MARL framework uses model predictive control (MPC) to prevent actions that could lead to collisions during training and execution. We demonstrate the effectiveness of our method in simulation and on real robots, achieving safe behavior-based cooperative navigation without using individual reference targets, with zero collisions, and faster target reaching compared to baselines. Finally, we study the impact of MPC safety filters on the learning process, revealing that we achieve faster convergence during training and we show that our approach can be safely deployed on real robots, even during early stages of the training.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
