Research on Search Algorithm Using Particle Swarm Optimization with Virtual Pheromone for Swarm Robots

Abstract

This paper proposes a search algorithm using particle swarm optimization (PSO) with virtual pheromone for swarm robots. Swarm robots are attracting attention in disaster relief works to search for victims. The search algorithm involves a combination of global and local searching. The conventional search method consists of random walk as the global search and PSO as the local search. However, random walk is not efficient in complex environments. For efficient searching, PSO with virtual pheromone is used for the global search. The virtual pheromone drives the swarm robots to an unsearched area, dose not need map data, and has low calculation cost. In addition, it is not necessary in the proposed method to switch algorithms between global and local searching. The validity of the proposed method was confirmed from the simulation results.