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This paper introduces PSANE, a Proprioceptive Safe Active Navigation and Exploration framework, for legged robots navigating deformable granular terrains. PSANE uses Gaussian Process regression to learn a traversability model from leg-terrain interaction measurements, enabling online estimation of safe regions and exploration frontiers. The framework integrates these estimates with a reactive controller and a multi-objective frontier selection strategy, demonstrating improved navigation performance compared to baselines in unknown granular environments.
Legged robots can now safely explore unknown, deformable terrain using only proprioceptive feedback to estimate traversability, outperforming traditional methods.
Deformable granular terrains introduce significant locomotion and immobilization risks in planetary exploration and are difficult to detect via remote sensing (e.g., vision). Legged robots can sense terrain properties through leg-terrain interactions during locomotion, offering a direct means to assess traversability in deformable environments. How to systematically exploit this interaction-derived information for navigation planning, however, remains underexplored. We address this gap by presenting PSANE, a Proprioceptive Safe Active Navigation and Exploration framework that leverages leg-terrain interaction measurements for safe navigation and exploration in unknown deformable environments. PSANE learns a traversability model via Gaussian Process regression to estimate and certify safe regions and identify exploration frontiers online, and integrates these estimates with a reactive controller for real-time navigation. Frontier selection is formulated as a multi-objective optimization that balances safe-set expansion probability and goal-directed cost, with subgoals selected via scalarization over the Pareto-optimal frontier set. PSANE safely explores unknown granular terrain and reaches specified goals using only proprioceptively estimated traversability, while achieving performance improvements over baseline methods.
