Bacterial foraging oriented by particle swarm optimization of a Lyapunov-based controller for mobile robot target tracking

Target tracking is a challenging task for an autonomous mobile robot moving in unknown environments. This paper addresses an efficient bacterial foraging oriented by a particle swarm optimization algorithm for an automatic tuning of the control gains of a Lyapunov-based controller in order to enable a mobile robot to track a moving target along an unknown trajectory. The proposed optimization algorithm combines both BFO and PSO algorithms advantages to increase the possibility of finding the optimal values within a short computational time and avoid to be trapped in local minima. Simulation results illustrate that the proposed approach is efficient and effective; and leads to good target tracking performances.