Joint sensor fault detection and recovery based on virtual sensor for walking legged robots

Abstract

This paper presents a novel method for joint sensor faults detection and faulty signal reconstruction. It uses the Virtual Joint Sensor (VJS). The model structure of the VJS consists of two interconnected models: The simple Linear Inverted Pendulum Model (LIPM) and the robot leg kinematics model (LKM). Kalman filter based on LIPM estimates the Center of Mass (CoM) position of the biped. The LKM uses the estimated CoM position to calculate the joints angles. A faulty signal model is formed to detect the faults, based on an adaptive threshold, and recovers the signal using the VJS outputs. The sensor abrupt, incipient, and frozen output faults are studied and tested. The validity of the proposed method was confirmed by simulations on 3D dynamics model of the humanoid robot SURALP while walking on a flat terrain.