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Published July 2020 | Submitted
Journal Article Open

GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning

Abstract

We present GLAS: Global-to- Local Autonomy Synthesis, a provably-safe, automated distributed policy generation for multi-robot motion planning. Our approach combines the advantage of centralized planning of avoiding local minima with the advantage of decentralized controllers of scalability and distributed computation. In particular, our synthesized policies only require relative state information of nearby neighbors and obstacles, and compute a provably-safe action. Our approach has three major components: i) we generate demonstration trajectories using a global planner and extract local observations from them, ii) we use deep imitation learning to learn a decentralized policy that can run efficiently online, and iii) we introduce a novel differentiable safety module to ensure collision-free operation, thereby allowing for end-to-end policy training. Our numerical experiments demonstrate that our policies have a 20% higher success rate than optimal reciprocal collision avoidance, ORCA, across a wide range of robot and obstacle densities. We demonstrate our method on an aerial swarm, executing the policy on low-end microcontrollers in real-time.

Additional Information

© 2020 IEEE. Manuscript received February 24, 2020; accepted April 20, 2020. Date of publicationMay 11, 2020; date of current version May 25, 2020. This letter was recommended for publication by Associate Editor M. Ani Hsieh and Editor N.Y. Chong upon evaluation of the reviewers' comments. This work was supported by the Raytheon Company and Caltech/NASA Jet Propulsion Laboratory. Video: https://youtu.be/z9LjSfLfG6c. Code: https://github.com/bpriviere/glas.

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August 19, 2023
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October 20, 2023