Fall Prediction for Bipedal Robots: The Standing Phase

TL;DR

This paper introduces a CNN-based method for early fall prediction in bipedal robots during standing, capable of detecting various faults with minimal false positives, thereby improving safety and reliability.

Contribution

The paper presents a novel CNN algorithm that detects multiple fault types and estimates fall lead time in humanoid robots, validated through simulation and hardware data.

Findings

Achieved high lead times and response times across fault scenarios.

False positive rate of zero in evaluations.

Effective detection of abrupt, incipient, and intermittent faults.

Abstract

This paper presents a novel approach to fall prediction for bipedal robots, specifically targeting the detection of potential falls while standing caused by abrupt, incipient, and intermittent faults. Leveraging a 1D convolutional neural network (CNN), our method aims to maximize lead time for fall prediction while minimizing false positive rates. The proposed algorithm uniquely integrates the detection of various fault types and estimates the lead time for potential falls. Our contributions include the development of an algorithm capable of detecting abrupt, incipient, and intermittent faults in full-sized robots, its implementation using both simulation and hardware data for a humanoid robot, and a method for estimating lead time. Evaluation metrics, including false positive rate, lead time, and response time, demonstrate the efficacy of our approach. Particularly, our model achieves impressive lead times and response times across different fault scenarios with a false positive rate of 0. The findings of this study hold significant implications for enhancing the safety and reliability of bipedal robotic systems.