Learning-Based Fault Detection for Legged Robots in Remote Dynamic Environments

TL;DR

This paper introduces a learning-based method for detecting limb faults in quadruped robots using proprioceptive data, enabling adaptive gait adjustments in hazardous environments.

Contribution

It presents an off-line fault detection approach that helps quadruped robots autonomously identify limb damage and select suitable gaits based on physical condition.

Findings

Successfully detects single limb faults from sensor data

Enables robots to adapt gait in response to detected faults

Improves robot survivability in complex environments

Abstract

Operations in hazardous environments put humans, animals, and machines at high risk for physically damaging consequences. In contrast to humans and animals, quadruped robots cannot naturally identify and adjust their locomotion to a severely debilitated limb. The ability to detect limb damage and adjust movement to a new physical morphology is the difference between survival and death for humans and animals. The same can be said for quadruped robots autonomously carrying out remote assignments in dynamic, complex settings. This work presents the development and implementation of an off-line learning-based method to detect single limb faults from proprioceptive sensor data in a quadrupedal robot. The aim of the fault detection technique is to provide the correct output for the controller to select the appropriate tripedal gait to use given the robot's current physical morphology.