Risk-aware planetary rover operation: Autonomous terrain classification and path planning

Masahiro Ono, Thoams J. Fuchs, Amanda Steffy, Mark Maimone, Jeng Yen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

81 Scopus citations


Identifying and avoiding terrain hazards (e.g., soft soil and pointy embedded rocks) are crucial for the safety of planetary rovers. This paper presents a newly developed ground-based Mars rover operation tool that mitigates risks from terrain by automatically identifying hazards on the terrain, evaluating their risks, and suggesting operators safe paths options that avoids potential risks while achieving specified goals. The tool will bring benefits to rover operations by reducing operation cost, by reducing cognitive load of rover operators, by preventing human errors, and most importantly, by significantly reducing the risk of the loss of rovers. The risk-aware rover operation tool is built upon two technologies. The first technology is a machine learning-based terrain classification that is capable of identifying potential hazards, such as pointy rocks and soft terrains, from images. The second technology is a risk-aware path planner based on rapidly-exploring random graph (RRG) and the A∗ search algorithms, which is capable of avoiding hazards identified by the terrain classifier with explicitly considering wheel placement. We demonstrate the integrated capability of the proposed risk-aware rover operation tool by using the images taken by the Curiosity rover.

Original languageEnglish
Title of host publication2015 IEEE Aerospace Conference, AERO 2015
PublisherIEEE Computer Society
ISBN (Electronic)9781479953790
StatePublished - 5 Jun 2015
Externally publishedYes
Event2015 IEEE Aerospace Conference, AERO 2015 - Big Sky, United States
Duration: 7 Mar 201514 Mar 2015

Publication series

NameIEEE Aerospace Conference Proceedings
ISSN (Print)1095-323X


Conference2015 IEEE Aerospace Conference, AERO 2015
Country/TerritoryUnited States
CityBig Sky


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