Abstract
When searching for multiple targets in an unknown complex environment, swarm robots should firstly form a number of subswarms autonomously through a task division model and then each subswarm searches for a target in parallel. Based on the probability response principle and multitarget division strategy, a closed-loop regulation strategy is proposed, which includes target type of member, target response intensity evaluation, and distance to the corresponding individuals. Besides, it is necessary to make robots avoid other robots and convex obstacles with various shapes in the unknown complex environment. By decomposing the multitarget search behavior of swarm robots, a simplified virtual-force model (SVF-Model) is developed for individual robots, and a control method is designed for swarm robots searching for multiple targets (SRSMT-SVF). The simulation results indicate that the proposed method keeps the robot with a good performance of collision avoidance, effectively reducing the collision conflicts among the robots, environment, and individuals.
Highlights
Swarm robotics is inspired by the self-organization behavior of social animals such as ants and bees
In case of multitarget search task, the situation will be different. e swarm robots must first be divided into several subswarms through the task division strategy and search for their respective targets collaboratively [12, 13]
Simulation experiments are performed in the environment containing convex obstacles with various shapes, and the results demonstrate that the swarm robots searching for multiple targets based on the proposed simplified virtualforce model (SRSMT-SVF) can reasonably configure the robot resource level and effectively avoid collisions among the internal members of the system and between the system and the environment
Summary
Swarm robotics is inspired by the self-organization behavior of social animals such as ants and bees. Complexity strategy is developed by Derr et al [14] and the subswarms cooperatively search for their targets by the particle swarm algorithm principle. Zhang et al [15] proposed a dynamic task division strategy with closed-loop regulation for multitarget search of swarm robots, which considered the coordination of the subswarm. This strategy removed some advantageous individual members when adjusting the size of the sub_swarm and did not take into account of some underlying behaviors, such as collision and roaming. Simulation experiments are performed in the environment containing convex obstacles with various shapes, and the results demonstrate that the swarm robots searching for multiple targets based on the proposed simplified virtualforce model (SRSMT-SVF) can reasonably configure the robot resource level and effectively avoid collisions among the internal members of the system and between the system and the environment
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