STEP: Stochastic Traversability Evaluation and Planning for Safe Off-road Navigation

• URL: http://arxiv.org/abs/2103.02828v1
• Date: Thu, 4 Mar 2021 04:24:19 GMT
• Title: STEP: Stochastic Traversability Evaluation and Planning for Safe Off-road Navigation
• Authors: David D. Fan, Kyohei Otsu, Yuki Kubo, Anushri Dixit, Joel Burdick, and Ali-Akbar Agha-Mohammadi
• Abstract summary: We propose an approach for assessing traversability and planning a safe, feasible, and fast trajectory in real-time. Our approach 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. We analyze our method in simulation and validate its efficacy on wheeled and legged robotic platforms exploring extreme terrains including an underground lava tube.
• Score: 9.423950528323893
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 underground lava tube.

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