ProNav: Proprioceptive Traversability Estimation for Legged Robot Navigation in Outdoor Environments

TL;DR

ProNav is a novel proprioceptive-based method for legged robot navigation that improves terrain stability assessment, crash prediction, and energy efficiency in outdoor environments by leveraging joint sensors.

Contribution

It introduces a new proprioceptive approach for traversability estimation, integrating sensor data to enhance stability, safety, and energy efficiency in outdoor terrain navigation.

Findings

Up to 40% improvement in success rate

Up to 15.1% reduction in energy consumption

Effective crash prediction and terrain assessment

Abstract

We propose a novel method, ProNav, which uses proprioceptive signals for traversability estimation in challenging outdoor terrains for autonomous legged robot navigation. Our approach uses sensor data from a legged robot's joint encoders, force, and current sensors to measure the joint positions, forces, and current consumption respectively to accurately assess a terrain's stability, resistance to the robot's motion, risk of entrapment, and crash. Based on these factors, we compute the appropriate robot gait to maximize stability, which leads to reduced energy consumption. Our approach can also be used to predict imminent crashes in challenging terrains and execute behaviors to preemptively avoid them. We integrate ProNav with an exteroceptive-based method to navigate real-world environments with dense vegetation, high granularity, negative obstacles, etc. Our method shows an improvement up to 40% in terms of success rate and up to 15.1% reduction in terms of energy consumption compared to exteroceptive-based methods.