Sensor Anomaly Detection for Biped Robot Using the Dynamic Equation of a Robotic System

Falling down is a critical event for biped robots. A robot can fall down easily when one of the sensors is out of order. This paper aims to develop an anomaly detection method for biped robots that can detect anomalies in sensor outputs. A large literature on anomaly detection exists: machine learning algorithms, statistical techniques, and various methods have been developed to detect anomalies. However, each technique performs well under certain conditions, and unfortunately none of these has proven to be efficient in accurately detecting anomalies in real-world situations for robotic systems. In this paper, we introduce a novel approach to detecting anomalies in a biped robot, based on the dynamic equation of a robotic system coupled with a robust z-score. Our method achieved unsupervised and real-time anomaly detection with great accuracy.