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STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation
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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.
Forward citations
Cited by 4 Pith papers
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Implicit Dual-Control for Visibility-Aware Navigation in Unstructured Environments
VA-MPPI is a model predictive path integral controller that uses predicted visibility to update terrain uncertainty inside each rollout, showing in simulation fewer collisions in occluded environments than a determini...
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Online Adaptive Traversability Estimation through Interaction for Unstructured, Densely Vegetated Environments
A robot can train a 3D voxel-based traversability model from scratch in situ from self-supervised collision data in about eight minutes, achieving MCC 0.63 and enabling safe point-to-point navigation in dense vegetation.
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CoCoNav: Conformal Control for Safe Robot Navigation in Crowds
CoCoNav combines horizon-specific conformal PI calibration with a relax-then-verify MPC planner to provide runtime-certified crowd navigation.
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