Hybrid Odor Detection System for Search and Rescue Robot Based on PSO

摘要

Aiming at the problem of the search and rescue robots' perception of various gases in chemical engineeringsites, a hybrid odor detection system for search and rescue robots based on particle swarm optimization wasproposed. In order to improve the stability and prediction accuracy of the system, a method of using particleswarm (PSO) to optimize the weighting coefficients of the integrated neural network is proposed, that is, usingthe global search ability of PSO and introducing an improved PSO algorithm to optimize the weights andthresholds of the BP neural network on the basis of the original BP neural network, and the optimized networkis used in the detection system, thus reducing the detection error of the system. The system analyzed theresponse signals of the 4 gas mixtures of the sensor array, the experimental results show that the neuralnetwork algorithm based on particle swarm optimization is applied to the training of gas mixture quantitativeidentification, the convergence speed is faster and the detection accuracy is higher than that of BP neuralnetwork algorithm.