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NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge

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arxiv 2103.11470 v4 pith:D37TCY2C submitted 2021-03-21 cs.RO cs.AI

Ali Agha , Kyohei Otsu , Benjamin Morrell , David D. Fan , Rohan Thakker , Angel Santamaria-Navarro , Sung-Kyun Kim , Amanda Bouman
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Xianmei Lei Jeffrey Edlund Muhammad Fadhil Ginting Kamak Ebadi Matthew Anderson Torkom Pailevanian Edward Terry Michael Wolf Andrea Tagliabue Tiago Stegun Vaquero Matteo Palieri Scott Tepsuporn Yun Chang Arash Kalantari Fernando Chavez Brett Lopez Nobuhiro Funabiki Gregory Miles Thomas Touma Alessandro Buscicchio Jesus Tordesillas Nikhilesh Alatur Jeremy Nash William Walsh Sunggoo Jung Hanseob Lee Christoforos Kanellakis John Mayo Scott Harper Marcel Kaufmann Anushri Dixit Gustavo Correa Carlyn Lee Jay Gao Gene Merewether Jairo Maldonado-Contreras Gautam Salhotra Maira Saboia Da Silva Benjamin Ramtoula Yuki Kubo Seyed Fakoorian Alexander Hatteland Taeyeon Kim Tara Bartlett Alex Stephens Leon Kim Chuck Bergh Eric Heiden Thomas Lew Abhishek Cauligi Tristan Heywood Andrew Kramer Henry A. Leopold Chris Choi Shreyansh Daftry Olivier Toupet Inhwan Wee Abhishek Thakur Micah Feras Giovanni Beltrame George Nikolakopoulos David Shim Luca Carlone Joel Burdick
This is my paper · ORCID
classification cs.ROcs.AI
keywords nebulaautonomycostardiscusssubterraneanchallengedarpaplanning
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved 2nd and 1st place, respectively. We also discuss CoSTAR's demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including: (i) geometric and semantic environment mapping; (ii) a multi-modal positioning system; (iii) traversability analysis and local planning; (iv) global motion planning and exploration behavior; (i) risk-aware mission planning; (vi) networking and decentralized reasoning; and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g. wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.

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Cited by 6 Pith papers

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