受损多足机器人可自检并自动更新模型，仅靠廉价惯性传感器
Robust Embodied Self-Identification of Morphology in Damaged Multi-Legged Robots
- 用快速傅里叶变换滤波器处理不一致信号，提升损伤检测精度
- 在不平地形上验证，能准确识别缺失腿并更新控制模型
- 适合资源受限的野外机器人，无需高成本感知设备
多足机器人在复杂任务中易发生腿部损伤，影响性能。本文提出一种自建模与损伤识别算法，仅使用低成本惯性测量单元（IMU）数据，实现对部分或完全失去腿部的自主适应。引入新型基于快速傅里叶变换（FFT）的滤波器，解决信号时间不一致问题，通过比较机器人本体与模型的躯干姿态差异来提升损伤检测效果。该方法可识别受损腿部，并将更新后的模型集成至控制系统。在不平地形上的实验验证了其鲁棒性与计算效率。
原文摘要 · Abstract (English)
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.
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