多智能体协作采集数据，自动优化分工与区域划分以减少信息刷新延迟。
Multi-agent coordination for data gathering with periodic requests and deliveries
- 分角色协作：工人采集数据，收集者循环传输数据至中心。
- 根据可用智能体数量，动态划分工作区并分配角色以最小化刷新时间。
- 适用于通信受限场景，适合需定期获取更新信息的监测系统。
本演示工作提出一种方法，用于规划和协调多智能体团队按需采集信息。数据由静态指挥中心（OC）周期性地从变动的目标位置请求。团队任务是前往这些位置采集测量数据，并将数据传回给OC。由于通信范围有限以及障碍物导致的信号衰减，智能体必须返回至OC上传数据。智能体可扮演两种角色：工人负责采集数据，收集者则沿固定路径移动，收集工人的数据并重新传回给OC。所交付信息的刷新时间取决于可用智能体数量及具体场景。提出的算法在规划阶段确定最优的收集者-工人配比及场景分区，以实现最小刷新时间，该方案将由智能体执行。
原文摘要 · Abstract (English)
In this demo work we develop a method to plan and coordinate a multi-agent team to gather information on demand. The data is periodically requested by a static Operation Center (OC) from changeable goals locations. The mission of the team is to reach these locations, taking measurements and delivering the data to the OC. Due to the limited communication range as well as signal attenuation because of the obstacles, the agents must travel to the OC, to upload the data. The agents can play two roles: ones as workers gathering data, the others as collectors traveling invariant paths for collecting the data of the workers to re-transmit it to the OC. The refreshing time of the delivered information depends on the number of available agents as well as of the scenario. The proposed algorithm finds out the best balance between the number of collectors-workers and the partition of the scenario into working areas in the planning phase, which provides the minimum refreshing time and will be the one executed by the agents.
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