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.

翻译：本文提出了一种针对双足机器人摔倒预测的新方法，专门检测由突发、早期和间歇性故障引发的站立阶段潜在摔倒。利用一维卷积神经网络（CNN），我们的方法旨在最大化摔倒预测的前置时间，同时最小化误报率。所提出的算法独特地集成了多种故障类型的检测，并估计了潜在摔倒的前置时间。我们的贡献包括：开发了一种能够检测全尺寸机器人突发、早期和间歇性故障的算法；使用仿真数据和硬件数据在人形机器人上实现了该算法；以及提出了一种估计前置时间的方法。包括误报率、前置时间和响应时间在内的评估指标证明了我们方法的有效性。特别是在不同故障场景下，我们的模型在误报率为0的情况下实现了令人印象深刻的前置时间和响应时间。本研究的发现对于提升双足机器人系统的安全性和可靠性具有重要意义。