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Although autonomy has gained widespread usage in structured and controlledenvironments, robotic autonomy in unknown and off-road terrain remains adifficult problem. Extreme, off-road, and unstructured environments such asundeveloped wilderness, caves, rubble, and other post-disaster sites poseunique and challenging problems for autonomous navigation. Based on ourparticipation in the DARPA Subterranean Challenge, we propose an approach toimprove autonomous traversal of robots in subterranean environments that areperceptually degraded and completely unknown through a traversability andplanning framework called STEP (Stochastic Traversability Evaluation andPlanning). We present 1) rapid uncertainty-aware mapping and traversabilityevaluation, 2) tail risk assessment using the Conditional Value-at-Risk (CVaR),3) efficient risk and constraint-aware kinodynamic motion planning usingsequential quadratic programming-based (SQP) model predictive control (MPC), 4)fast recovery behaviors to account for unexpected scenarios that may causefailure, and 5) risk-based gait adaptation for quadrupedal robots. Weillustrate and validate extensive results from our experiments on wheeled andlegged robotic platforms in field studies at the Valentine Cave, CA (caveenvironment), Kentucky Underground, KY (mine environment), and Louisville MegaCavern, KY (final competition site for the DARPA Subterranean Challenge withtunnel, urban, and cave environments).

STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation; Results from the DARPA Subterranean Challenge