Mobile robot communication fault diagnosis method based on swarm intelligence algorithm

Abstract

The current communication fault diagnosis methods mainly focus on the classifier with fault probability, which often leads to the low diagnosis efficiency. In order to overcome the above problems, mobile robot communication fault diagnosis method based on swarm intelligence algorithm is proposed in the paper. Firstly, the abnormal data is extracted after the analysis of the communication data with Kalman filter. Secondly, the supporting decision model was designed to standardize the communication exchange process and locate the fault range. Futher more, the ant colony optimization algorithm combined with particle swarm optimization was used to locate the fault area, and the multi-model hybrid method was adopted to comprehensively judge the communication fault. The different interference ratio was used in the experiment to test the performance of the proposed algorithm compared with the SVM and Bayesian model. Finally, the experiment results show the validity of mobile robort communication fault diagnosis based on swarm intelligence algorithm.