STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation
Abstract
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.
Additional Information
© 2021 Robotics Science & Systems Foundation. The work is performed at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004), and Defense Advanced Research Projects Agency (DARPA).Attached Files
Published - p021.pdf
Submitted - 2103.02828.pdf
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Additional details
- Alternative title
- STEP: Stochastic Traversability Evaluation and Planning for Safe Off-road Navigation
- Eprint ID
- 111307
- Resolver ID
- CaltechAUTHORS:20211008-222405905
- 80NM0018D0004
- NASA
- NASA/JPL/Caltech
- Defense Advanced Research Projects Agency (DARPA)
- Created
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2021-10-08Created from EPrint's datestamp field
- Updated
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2021-10-08Created from EPrint's last_modified field