STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation
arXiv:2103.02828
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.
arXiv commentAccepted to Robotics: Science and Systems (RSS) 2021. Video link: https://youtu.be/N97cv4eH5c8
References in corpus (7)
- OSQP: An Operator Splitting Solver for Quadratic Programs
- Distributional Reinforcement Learning with Quantile Regression
- Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach
- Fast Approximate Clearance Evaluation for Rovers with Articulated Suspension Systems
- NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge
- PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments
- Constrained Risk-Averse Markov Decision Processes