Long-Range Indoor Navigation With PRM-RL

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...

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Bibliographic Details
Published in:IEEE transactions on robotics 2020-08, Vol.36 (4), p.1115-1134
Main Authors: Francis, Anthony, Faust, Aleksandra, Chiang, Hao-Tien Lewis, Hsu, Jasmine, Kew, J. Chase, Fiser, Marek, Lee, Tsang-Wei Edward
Format: Article
Language:English
Subjects:
Buildings
Collision avoidance
Indoor navigation
Navigation
Obstacle avoidance
Planning
Probabilistic roadmaps (PRMs)
reinforcement learning (RL)
Robot sensing systems
robotics
Robots
Sampling
sampling-based planning
Sensors
Simulation
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Summary: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.
ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2020.2975428