Swarm Robots Search for Multiple Targets

This paper addresses the challenge of swarm robots search for multiple targets simultaneously. Techniques are investigated gradually and a systematic scheme which is based on mechanical particle swarm optimization and artificial potential field is eventually developed. The innovative extension makes the bio-inspired particle swarm optimization first endowed with the robots' mechanical properties which reduces the control expense and is already beyond the conventional application scope of this algorithm. The scheme closely considers the practical applications of real robots thus uses the differences, for example, signal frequencies, between the targets for organizing corresponding sub-robot groups aiming at different targets. Those robot groups which move towards non-aimed targets are applied with penalties thus an unimodal objective function for each robot group is built. Meanwhile, the developed method contains the ability for obstacle avoidance based on the module-switching strategy according to their priorities. The methods for controlling the group size and make balance of the search convergence and diversity are investigated, too. Rich simulations and experiments with real robots have been performed to verify this work. Promising results show the effectiveness and robustness of the proposed search method.