The problem of multi-robot navigation of connectivity maintenance is challenging in multi-robot applications. This work investigates how to navigate a multi-robot team in unknown environments while maintaining connectivity. We propose a reinforcement learning (RL) approach to develop a decentralized policy, which is shared among multiple robots. Given range sensor measurements and the positions of other robots, the policy aims to generate control commands for navigation and preserve the global connectivity of the robot team. We incorporate connectivity concerns into the RL framework as constraints and introduce behavior cloning to reduce the exploration complexity of policy optimization. The policy is optimized with all transition data collected by multiple robots in random simulated scenarios. We validate the effectiveness of the proposed approach by comparing different combinations of connectivity constraints and behavior cloning. We also show that our policy can generalize to unseen scenarios in both simulation and holonomic robots experiments.
翻译:多机器人连接维护多机器人导航问题在多机器人应用中具有挑战性。 这项工作调查了如何在未知环境中驾驶多机器人团队,同时保持连通性。 我们提出一种强化学习(RL)方法,以制定分散化的政策,由多个机器人共享。 鉴于范围感应测量和其他机器人的位置,该政策旨在生成导航控制指令并维护机器人团队的全球连通性。 我们把连通问题作为制约因素纳入RL框架,并引入行为克隆以减少政策优化的探索复杂性。 该政策优化了由多个机器人在随机模拟情景中收集的所有过渡数据。 我们通过比较连接限制和行为克隆的不同组合来验证拟议方法的有效性。 我们还表明,我们的政策可以在模拟和克隆机器人实验中概括为看不见的情形。