Learning-Based Fault Detection for Legged Robots in Remote Dynamic Environments
For quadruped robots operating autonomously in hazardous environments, this work addresses the critical need for fault detection to adapt locomotion to damaged limbs.
The paper develops an offline learning-based method to detect single limb faults in quadruped robots using proprioceptive sensor data, enabling the selection of appropriate tripedal gaits. The approach aims to improve robot survival in remote dynamic environments.
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.