Controlling swarm robots for target search in parallel and asynchronously

To control swarm robots with extended particle swarm optimization approach for target search, target signals should be detected and fused as fitness evaluate due to the inherent parallel processing property caused by spatial interspersed of robots in search environment. Also, differences in sampling frequency of sensors and communication delays make it realistic to control such swarm systems asynchronously. Therefore, two asynchronous update principles, i.e., the communication cycle-based and evolution position-based control strategies are presented in case of target search. Besides, a concept of time-varying character swarm is proposed to facilitate decisionmaking on the best-found position. Each robot detects signals in a fine-grained parallel way and compares fusion of signals with the best in its character swarm. Then velocities and positions of individual robots are updated immediately. But the shared information within character swarm is updated asynchronously according to different control principles only. Simulation results indicate that the communication cycle-based strategy has advantage over the evolution position-based control strategy in search ef-