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Team CERBERUS Wins the DARPA Subterranean Challenge: Technical Overview and Lessons Learned

Marco Tranzatto, Mihir Dharmadhikari, Lukas Bernreiter, Marco Camurri, Shehryar Khattak, Frank Mascarich, Patrick Pfreundschuh, David Wisth, Samuel Zimmermann, Mihir Kulkarni, Victor Reijgwart, Benoît Casseau, Timon Homberger, Paolo De Petris, Lionel Ott, Wayne Tubby, Gabriel Waibel, Huan X. Nguyen, César Cadena, Russell Buchanan

发表年份
2022
引用次数
15
访问权限
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摘要

This article presents the CERBERUS robotic system-of-systems, which won the DARPA Subterranean Challenge Final Event in 2021. The Subterranean Challenge was organized by DARPA with the vision to facilitate the novel technologies necessary to reliably explore diverse underground environments despite the grueling challenges they present for robotic autonomy. Due to their geometric complexity, degraded perceptual conditions combined with lack of GPS support, austere navigation conditions, and denied communications, subterranean settings render autonomous operations particularly demanding. In response to this challenge, we developed the CERBERUS system which exploits the synergy of legged and flying robots, coupled with robust control especially for overcoming perilous terrain, multi-modal and multi-robot perception for localization and mapping in conditions of sensor degradation, and resilient autonomy through unified exploration path planning and local motion planning that reflects robot-specific limitations. Based on its ability to explore diverse underground environments and its high-level command and control by a single human supervisor, CERBERUS demonstrated efficient exploration, reliable detection of objects of interest, and accurate mapping. In this article, we report results from both the preliminary runs and the final Prize Round of the DARPA Subterranean Challenge, and discuss highlights and challenges faced, alongside lessons learned for the benefit of the community.

关键词

RobotComputer scienceAutonomyHuman–computer interactionSupervisorExploitMotion planningTerrainPerceptionArtificial intelligence

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