Using the Particle Swarm Optimization Algorithm for Robotic Search Applications

This paper describes the experimental results of using the Particle Swarm Optimization (PSO) algorithm to control a suite of robots. In our approach, each hot is one particle in the PSO; each particle/bot makes measurements, updates its own position and velocity, updates its own personal best measurement (pbest) and personal best location (if necessary), and broadcasts to the other bots if it has found a global best measurement/position. We built three bots and tested the algorithm by letting the bots find the brightest spot of light in the room. The tests show that using the PSO to control a swarm can successfully find the target, even in the presence of obstacles.