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This paper presents and discusses algorithms, hardware, and softwarearchitecture developed by the TEAM CoSTAR (Collaborative SubTerraneanAutonomous Robots), competing in the DARPA Subterranean Challenge.Specifically, it presents the techniques utilized within the Tunnel (2019) andUrban (2020) competitions, where CoSTAR achieved 2nd and 1st place,respectively. We also discuss CoSTAR's demonstrations in Martian-analog surfaceand subsurface (lava tubes) exploration. The paper introduces our autonomysolution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy).NeBula is an uncertainty-aware framework that aims at enabling resilient andmodular autonomy solutions by performing reasoning and decision making in thebelief space (space of probability distributions over the robot and worldstates). We discuss various components of the NeBula framework, including: (i)geometric and semantic environment mapping; (ii) a multi-modal positioningsystem; (iii) traversability analysis and local planning; (iv) global motionplanning 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 andlessons learned from fielding this solution in the challenging courses of theDARPA Subterranean Challenge competition.

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