Motion Planning Networks: Bridging the Gap Between Learning-Based and Classical Motion Planners

This article describes motion planning networks (MPNet), a computationally efficient, learning-based neural planner for solving motion planning problems.MPNet uses neural networks to learn general near-optimal heuristics for path planning in seen and unseen environments. It takes environment informa...

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Vydané v:	IEEE transactions on robotics Ročník 37; číslo 1; s. 48 - 66
Hlavní autori:	Qureshi, Ahmed Hussain, Miao, Yinglong, Simeonov, Anthony, Yip, Michael C.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.02.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Cloud computing Configurations Deep learning in robotics and automation Feasibility Learning learning and adaptive systems learning from demonstration motion and path planning Motion planning Neural networks Optimization Path planning Performance measurement Planning Probabilistic logic Robots Search methods Strategy Three dimensional models Training data
ISSN:	1552-3098, 1941-0468
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