一种用于森林灭火的高效动态多无人机集群方法
An Efficient Approach with Dynamic Multi-Swarm of UAVs for Forest Firefighting
AI总结：
本文提出MSCIDC方法，通过多集群协同信息驱动搜索、集群动态调节与分治扑灭控制，实现森林火灾快速探测与扑灭，相比最佳多无人机方法减少65%过火面积和60%任务时间。
摘要：
本文提出了一种多集群协同信息驱动搜索与分治扑灭控制方法（MSCIDC），旨在通过减少生物多样性、养分、土壤水分及其他无形效益的损失，实现森林火灾的更快速探测与扑灭。集群是由多架无人机（UAV）组成的协同群体，它们共同飞行以有效搜索并扑灭火灾。多集群协同信息驱动搜索采用多级搜索，包括协同信息驱动的探索与利用，以快速、准确地检测火灾位置。搜索层级根据热传感器获取的潜在火灾区域信息进行选择。在全局调节性排斥和集群间合并的辅助下，集群的动态性相比现有方法缩短了探测与扑灭时间。集群成员之间的局部吸引有助于未检测到火情的成员更快到达火灾位置，而分治扑灭控制确保所有参与灭火的成员获得不重叠的火灾扇区分配。MSCIDC的性能已在松树林模拟环境中与不同的多无人机方法进行了比较。性能结果清楚地表明，MSCIDC扑灭火灾的速度远快于多无人机方法。蒙特卡洛模拟结果表明，与多无人机方法中的最佳结果相比，所提方法将平均森林过火面积减少了65%，任务时间缩短了60%，从而保证了更快速、更成功的任务执行。
英文摘要：
In this paper, the Multi-Swarm Cooperative Information-driven search and Divide and Conquer mitigation control (MSCIDC) approach is proposed for faster detection and mitigation of forest fire by reducing the loss of biodiversity, nutrients, soil moisture, and other intangible benefits. A swarm is a cooperative group of Unmanned Aerial Vehicles (UAVs) that fly together to search and quench the fire effectively. The multi-swarm cooperative information-driven search uses a multi-level search comprising cooperative information-driven exploration and exploitation for quick/accurate detection of fire location. The search level is selected based on the thermal sensor information about the potential fire area. The dynamicity of swarms, aided by global regulative repulsion and merging between swarms, reduces the detection and mitigation time compared to the existing methods. The local attraction among the members of the swarm helps the non-detector members to reach the fire location faster, and divide-and-conquer mitigation control ensures a non-overlapping fire sector allocation for all members quenching the fire. The performance of MSCIDC has been compared with different multi-UAV methods using a simulated environment of pine forest. The performance clearly shows that MSCIDC mitigates fire much faster than the multi-UAV methods. The Monte-Carlo simulation results indicate that the proposed method reduces the average forest area burnt by $65\%$ and mission time by $60\%$ compared to the best result case of the multi-UAV approaches, guaranteeing a faster and successful mission.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
