通过学习感知数据识别四足机器人单肢故障并自适应切换步态。
Learning-Based Fault Detection for Legged Robots in Remote Dynamic Environments
- 基于本体感觉传感器数据离线训练故障检测模型。
- 可准确识别单肢损伤并触发三足步态调整。
- 适合远程动态环境中自主作业的四足机器人使用。
危险环境中的作业对人类、动物和机器都构成严重物理伤害风险。与人和动物不同，四足机器人无法自然识别并调整因肢体严重受损导致的运动模式。能否检测肢体损伤并适应新的身体形态，是决定人类、动物乃至四足机器人在远程复杂动态环境中生存的关键。本文提出一种离线学习方法，利用本体感觉传感器数据检测四足机器人单肢故障。该故障检测技术旨在为控制器提供正确输出，使其根据当前身体形态选择合适的三足步态，从而提升机器人在极端环境下的自主生存能力。
原文摘要 · Abstract (English)
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.
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