Long-range indoor navigation requires guiding robots with noisy sensors and controls through cluttered environments along paths that span a variety of buildings. We achieve this with PRM-RL, a hierarchical robot navigation method in which reinforcement learning (RL) agents that map noisy sensors to robot controls learn to solve short-range obstacle avoidance tasks, and then sampling-based planners map where these agents can reliably navigate in simulation; these roadmaps and agents are then deployed on robots, guiding them along the shortest path where the agents are likely to succeed. In this article, we use probabilistic roadmaps (PRMs) as the sampling-based planner, and AutoRL as the RL method in the indoor navigation context. We evaluate the method with a simulation for kinematic differential drive and kinodynamic car-like robots in several environments, and on differential-drive robots at three physical sites. Our results show that PRM-RL with AutoRL is more successful than several baselines, is robust to noise, and can guide robots over hundreds of meters in the face of noise and obstacles in both simulation and on robots, including over 5.8 km of physical robot navigation.

中文翻译：

---

使用 PRM-RL 进行远程室内导航


远距离室内导航需要引导带有噪声传感器和控制装置的机器人沿着跨越各种建筑物的路径穿过杂乱的环境。我们通过 PRM-RL 实现这一目标，这是一种分层机器人导航方法，其中将噪声传感器映射到机器人控制的强化学习 (RL) 代理学习解决短程避障任务，然后基于采样的规划器映射这些代理可以可靠地执行的任务模拟导航；然后，这些路线图和代理被部署在机器人上，引导它们沿着代理可能成功的最短路径前进。在本文中，我们使用概率路线图 (PRM) 作为基于采样的规划器，并使用 AutoRL 作为室内导航环境中的 RL 方法。我们通过在多个环境中模拟运动差速驱动和运动动力学汽车式机器人以及在三个物理地点的差速驱动机器人来评估该方法。我们的结果表明，带有 AutoRL 的 PRM-RL 比几个基线更成功，对噪声具有鲁棒性，并且可以在模拟和机器人上面对噪声和障碍物时引导机器人超过数百米，包括超过 5.8 公里的物理机器人导航。