Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain

This letter presents a learning-based approach to consider the effect of unobservable world states in kinodynamic motion planning in order to enable accurate high-speed off-road navigation on unstructured terrain. Existing kinodynamic motion planners either operate in structured and homogeneous envi...

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Vydané v:	IEEE robotics and automation letters Ročník 6; číslo 3; s. 6054 - 6060
Hlavní autori:	Xiao, Xuesu, Biswas, Joydeep, Stone, Peter
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.07.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Autonomous vehicle navigation High speed Indoor environments Learning machine learning for robot control motion and path planning Motion control Motion planning Navigation Planning Robots Semantics Sensors Terrain Training
ISSN:	2377-3766, 2377-3766
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