A Fault Diagnosis Method for Quadruped Robot Based on Hybrid Deep Neural Networks

Abstract

The complex and precise mechanical mechanism of quadruped robots is prone to faults, which brings challenges to the reliability and stability of the system. Therefore, it is of significant to develop the fault diagnosis method for quadruped robots, which can provide effective fault information for active fault-tolerant control. In this article, we propose a novel fault detection and isolation method for quadruped robots based on convolution neural networks, gated recurrent units, and attention networks, which can detect and isolate joint faults in real time. The proposed method can automatically learn meaningful high-level spatial and temporal features from sensors data. The effectiveness of the method is verified by the Laikago robot compound fault data.