  Although ground robotic autonomy has gained widespread usage in structured
and controlled environments, autonomy in unknown and off-road terrain remains a
difficult problem. Extreme, off-road, and unstructured environments such as
undeveloped wilderness, caves, and rubble pose unique and challenging problems
for autonomous navigation. To tackle these problems we propose an approach for
assessing traversability and planning a safe, feasible, and fast trajectory in
real-time. Our approach, which we name STEP (Stochastic Traversability
Evaluation and Planning), relies on: 1) rapid uncertainty-aware mapping and
traversability evaluation, 2) tail risk assessment using the Conditional
Value-at-Risk (CVaR), and 3) efficient risk and constraint-aware kinodynamic
motion planning using sequential quadratic programming-based (SQP) model
predictive control (MPC). We analyze our method in simulation and validate its
efficacy on wheeled and legged robotic platforms exploring extreme terrains
including an abandoned subway and an underground lava tube.
