Terrain Classification for the Spot Quadrupedal Mobile Robot Using Only Proprioceptive Sensing

TL;DR

This paper presents a terrain classifier for the Boston Dynamics Spot quadruped robot that uses only proprioceptive signals to accurately identify terrain types, enhancing navigation safety.

Contribution

It introduces a novel terrain classification method utilizing proprioceptive data and dimensionality reduction for improved robot traversability mapping.

Findings

Achieved approximately 97% accuracy in terrain classification

Effectively differentiated three terrain types in field tests

Utilized only proprioceptive signals without external sensors

Abstract

Quadrupedal mobile robots can traverse a wider range of terrain types than their wheeled counterparts but do not perform the same on all terrain types. These robots are prone to undesirable behaviours like sinking and slipping on challenging terrains. To combat this issue, we propose a terrain classifier that provides information on terrain type that can be used in robotic systems to create a traversability map to plan safer paths for the robot to navigate. The work presented here is a terrain classifier developed for a Boston Dynamics Spot robot. Spot provides over 100 measured proprioceptive signals describing the motions of the robot and its four legs (e.g., foot penetration, forces, joint angles, etc.). The developed terrain classifier combines dimensionality reduction techniques to extract relevant information from the signals and then applies a classification technique to differentiate terrain based on traversability. In representative field testing, the resulting terrain classifier was able to identify three different terrain types with an accuracy of approximately 97%