Abstract
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.
Highlights
probabilistic roadmaps (PRMs)-reinforcement learning (RL)’s success rate is 89% on the dense PRM, a 97% relative increase over PRM-dynamic window avoidance (DWA) and a 157% increase over PRM-guided artificial potential field (GAPF). These results show that the performance of PRM-RL with an AutoRL policy is weak but comparable to its performance on floorplan maps, and exceeds all other baselines; it is even superior to PRM-hand tuned reinforcement learning (HTRL) on floorplan maps
We presented PRM-RL, a hierarchical planning method for long-range navigation that combines sampling-based path planning with RL agent as a local planner in very large environments
We evaluated the method on a differential drive and a car model with inertia used on floormaps from five building, two noisy obstacle maps, and on three physical testbed environments
Summary
To robustly navigate long distances in novel environments, we factor the problem into long-range path planning and end-to-end local control, while assuming the robot has mapping and localization. Long-range path planning finds collision-free paths to distant goals not reachable by local control [43]. End-to-end local control produces feasible controls to follow ideal paths while avoiding obstacles, e.g., [40] and [24], and compensating for noisy sensors and localization [12]. We enable end-to-end local control to inform long-range path planning through sampling-based planning. Date of publication April 15, 2020; date of current version August 5, 2020. This article was recommended for publication by Associate Editor F. Stulp and Editor Francois Chaumette upon evaluation of the reviewers’ comments. Stulp and Editor Francois Chaumette upon evaluation of the reviewers’ comments. (Corresponding author: Anthony Francis.)
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