Robust Embodied Self-Identification of Morphology in Damaged Multi-Legged Robots

TL;DR

This paper introduces a robust self-identification method for damaged multi-legged robots using low-cost IMU data and a novel FFT-based filter, enabling autonomous adaptation to leg damage during complex terrains.

Contribution

It presents a new self-modeling and damage detection algorithm that effectively identifies leg damage and updates the robot's model for improved resilience.

Findings

Accurately detects leg damage using IMU data

Effective on uneven terrain with high robustness

Computationally efficient for real-time application

Abstract

Multi-legged robots (MLRs) are vulnerable to leg damage during complex missions, which can impair their performance. This paper presents a self-modeling and damage identification algorithm that enables autonomous adaptation to partial or complete leg loss using only data from a low-cost IMU. A novel FFT-based filter is introduced to address time-inconsistent signals, improving damage detection by comparing body orientation between the robot and its model. The proposed method identifies damaged legs and updates the robot's model for integration into its control system. Experiments on uneven terrain validate its robustness and computational efficiency.