Proprioceptive Safe Active Navigation and Exploration for Planetary Environments

2026-03-09

AI Summary

This work addresses the challenge of navigating deformable granular terrains in planetary exploration, which are difficult to perceive via remote sensing and often lead to instability or entrapment of legged robots. To overcome this, the paper introduces the PSANE framework, which, for the first time, systematically leverages proprioceptive information from leg–terrain interactions to online-learn a traversability model. By employing Gaussian process regression, PSANE estimates safe regions and exploration frontiers in real time. A multi-objective optimization mechanism combines the probability of safe set expansion with goal-directed cost, scalarizing the Pareto front to select subgoals that enable a closed-loop system integrating safety certification and reactive motion control. Relying solely on proprioception, PSANE reliably and efficiently reaches target locations in unknown granular environments, significantly outperforming existing approaches.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• deformable terrain
• planetary exploration
• proprioceptive sensing
• safe navigation
• traversability

Innovation

• proprioceptive navigation
• legged robots
• deformable terrain
• Gaussian Process regression
• multi-objective exploration