多智能体协作实现按需数据采集与周期上传，自动分工优化效率。
Multi-agent coordination for on-demand data gathering with periodic information upload
- 分三步自动分配工者与收集者角色并规划路径。
- 实测在多种场景下优化了区域划分与人员配比。
- 适合需要动态调度的无人机/机器人数据采集系统。
本文提出一种多智能体团队协同部署方法，用于周期性按需采集动态目标位置的信息。静态运营中心（OC）定期向变动的目标地点请求数据，目标是采集目标信息并传回OC，同时平衡刷新时间与信息包总数。系统自动将团队分为两类角色：负责采集数据的工者，以及负责将数据中继至OC的收集者。所提三步法包括：1）确定工者的最优区域划分；2）求解工者与收集者之间的最优比例及通信对象（收集者或OC）；3）计算工者访问目标并交付数据至OC或移动中的收集者的最优巡游路径。在多种场景的仿真测试中验证了该方法的有效性，成功获得最优区域划分与工者-收集者配置方案。
原文摘要 · Abstract (English)
In this paper we develop a method for planning and coordinating a multi-agent team deployment to periodically gather information on demand. A static operation center (OC) periodically requests information from changing goal locations. The objective is to gather data in the goals and to deliver it to the OC, balancing the refreshing time and the total number of information packages. The system automatically splits the team in two roles: workers to gather data, or collectors to retransmit the data to the OC. The proposed three step method: 1) finds out the best area partition for the workers; 2) obtains the best balance between workers and collectors, and with whom the workers must to communicate, a collector or the OC; 3) computes the best tour for the workers to visit the goals and deliver them to the OC or to a collector in movement. The method is tested in simulations in different scenarios, providing the best area partition algorithm and the best balance between collectors and workers.
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