Swarm Robots Search for Multiple Targets Based on an Improved Grouping Strategy
Swarm robots search for multiple targets in collaboration in unknown environments has been addressed in this paper. An improved grouping strategy based on constriction factors Particle Swarm Optimization is proposed. Robots are grouped under this strategy after several iterations of stochastic movements, which considers the influence range of targets and environmental information they have sensed. The group structure may change dynamically and each group focuses on searching one target. All targets are supposed to be found finally. Obstacle avoidance is considered during the search process. Simulation compared with previous method demonstrates the adaptability, accuracy, and efficiency of the proposed strategy in multiple targets searching.
IEEE/ACM Transactions on Computational Biology and Bioinformatics