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.

中文翻译：

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未知复杂环境中群体机器人的多目标搜索

在未知复杂环境中搜索多个目标时，群机器人应首先通过任务划分模型自动形成多个子群，然后每个子群并行搜索目标。基于概率响应原理和多目标划分策略，提出了一种闭环调节策略，该策略包括成员的目标类型，目标响应强度评估以及与相应个体的距离。此外，在未知的复杂环境中，有必要使机器人避开其他机器人以及各种形状的凸形障碍物。通过分解群体机器人的多目标搜索行为，为单个机器人开发了简化的虚拟力模型（SVF-Model），并设计了一种针对群体机器人搜索多个目标的控制方法（SRSMT-SVF）。