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

We present GLAS: G lobal-to- L ocal A utonomy S ynthesis, 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...

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Veröffentlicht in:	IEEE robotics and automation letters Jg. 5; H. 3; S. 4249 - 4256
Hauptverfasser:	Riviere, Benjamin, Honig, Wolfgang, Yue, Yisong, Chung, Soon-Jo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.07.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Collision avoidance Distributed robot systems imitation learning Learning Measurement Microcontrollers Motion planning Multiple robots Optimal control path planning for multiple mobile robots or agents Planning Policies Robot dynamics Robots Safety State (computer science) Trajectory
ISSN:	2377-3766, 2377-3766
Online-Zugang:	Volltext
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