基于共识主动性的群体机器人目标搜索与围捕

群体机器人系统的目标搜索和围捕任务是智能机器人领域一个典型的复杂问题，大多数现有的解决这一问题的方法依赖于一些不现实的假设，如可靠的通信链接、全局坐标信息、已知的环境信息以及机器人之间的中央协调控制. 为此，本文提出了一种基于共识主动性的群体机器人目标搜索与围捕框架. 该框架对反蚁群算法进行了改进，加入了多种信息素来帮助群体机器人协作探索环境，并生成信息素地图. 同时，该框架把在前一阶段生成的信息素地图和分层基因调控网络(hierarchical gene regulatory network，H-GRN)模型相结合，完成了群体机器人在环境信息未知且通信受限的场景中对动态目标的搜索和围捕任务. 仿真实验表明，该方法相较于传统方法具有更好的性能表现. 

Stigmergy-Based Swarm Robots for Target Search and Trapping

As a classical but difficult problem, multi-target searching and entrapping in a swarm of robots have received more and more attention in recent years. However, most existing approaches for addressing this problem rely on unrealistic assumptions such as reliable communication links, available global coordinate system, known environmental information, and central coordination among robots. Therefore, in this paper, a stigmergy mechanism-based framework was proposed for the use of searching and entrapping targets in a swarm of robots. Improving the inverse ant colony system, the framework was designed by adding a variety of pheromones to help group robots to collaborate and explore the environment and generate pheromone maps. Meanwhile, combining the Hierarchical Gene Regulatory Network (H-GRN) model with pheromone maps generated in the previous stage, the framework was arranged for robotic systems to search and entrap dynamic targets in unknown and communication-limited environments. Simulation results show that, comparing with traditional methods, the proposed framework can achieve better performance in target searching and trapping.