When searching for multiple targets in an unknown complex environment, swarm robots should firstly form a number of\nsubswarms autonomously through a task division model and then each subswarm searches for a target in parallel. Based on the\nprobability response principle and multitarget division strategy, a closed-loop regulation strategy is proposed, which includes\ntarget type of member, target response intensity evaluation, and distance to the corresponding individuals. Besides, it is necessary\nto make robots avoid other robots and convex obstacles with various shapes in the unknown complex environment. By\ndecomposing the multitarget search behavior of swarm robots, a simplified virtual-force model (SVF-Model) is developed for\nindividual robots, and a control method is designed for swarm robots searching for multiple targets (SRSMT-SVF). The\nsimulation results indicate that the proposed method keeps the robot with a good performance of collision avoidance, effectively\nreducing the collision conflicts among the robots, environment, and individuals.
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