🤖 AI Summary
To address fall risks in bipedal robots during static standing caused by sudden, incipient, and intermittent faults, this paper proposes a high-temporal-resolution, zero-false-alarm fall prediction method. The method unifies the modeling of these three fault types and precisely estimates the pre-fall time interval. Implemented on a full-scale humanoid robot platform, it is validated both in simulation and on physical hardware. A one-dimensional convolutional neural network (1D CNN) processes time-series signals from plantar moments and body姿态 (orientation), enabling simultaneous multi-fault classification and fall-trend assessment. Experimental results demonstrate zero false alarms, an average pre-fall time of 320 ms, and an end-to-end system latency under 50 ms. This work significantly enhances proactive safety capabilities for bipedal robots operating in static standing scenarios.
📝 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.
