二足歩行ロボットの転倒予測：立位フェーズにおける新手法
Fall Prediction for Bipedal Robots: The Standing Phase
1D CNNを用いて、立位中の突発的・初期・断続的故障による転倒を予測し、リードタイムを最大化しつつ誤検知率を最小化する手法を提案。シミュレーションと実機データで有効性を検証。
著者: M. Eva Mungai, Gokul Prabhakaran, Jessy W. Grizzle
分類: cs.RO
原文アブストラクト
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.
