Proprioceptive Safe Active Navigation and Exploration for Planetary Environments

TL;DR

This paper introduces PSANE, a framework enabling legged robots to safely navigate and explore deformable granular terrains using proprioceptive leg-terrain interaction data, with a learned model for real-time traversability assessment.

Contribution

The paper presents a novel proprioceptive-based navigation framework that learns traversability models and plans safe exploration in unknown deformable environments, improving over baseline methods.

Findings

Successfully explores unknown granular terrain using proprioception.

Achieves safe navigation and goal reaching with performance gains.

Employs Gaussian Process regression for real-time traversability estimation.

Abstract

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.