MLNav: Learning to Safely Navigate on Martian Terrains

We present MLNav , a learning-enhanced path planning framework for safety-critical and resource-limited systems operating in complex environments, such as rovers navigating on Mars. MLNav makes judicious use of machine learning to enhance the efficiency of path planning while fully respecting safety...

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Veröffentlicht in:	IEEE robotics and automation letters Jg. 7; H. 2; S. 5461 - 5468
Hauptverfasser:	Daftry, Shreyansh, Abcouwer, Neil, Sesto, Tyler Del, Venkatraman, Siddarth, Song, Jialin, Igel, Lucas, Byon, Amos, Rosolia, Ugo, Yue, Yisong, Ono, Masahiro
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.04.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Computational modeling Feasibility Integrated planning and learning Machine learning Mars Mars rovers motion and path planning Navigation Path planning Planning Robots Safety Safety critical space robotics and automation
ISSN:	2377-3766, 2377-3766
Online-Zugang:	Volltext
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