Doorplate Recognition for a Mobile Robot Based on PSO and RBF Neural Network

Abstract

This paper proposes a mixed optimization algorithm based on RBF neural network (RBF) and Particle Swarm Optimization (PSO), which is applied to the doorplate recognition for a mobile robot. The centers and widths of RBF neural network are determined with self-increasing clustering algorithm, and the improved particle swarm optimization algorithm is used to optimize their distance from the threshold. Experimental results show that this algorithm has an advantage over traditional neural network algorithm in terms of accuracy recognition ratio and convergence rate. Hence, the proposed algorithm can meet the needs of robot vision system.