Aerial Vehicle Path Planning for Monitoring Wildfire Frontiers
Aerial Vehicle Path Planning for Monitoring Wildfire Frontiers
复制标题
用于监测野火边界的飞行器路径规划
DOI：
--
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Geoffrey A. Hollinger
中科院分区：
文献类型：
--
作者：
Ryan Skeele;Geoffrey A. Hollinger
本文探讨了无人驾驶飞行器（uav）在野火监测中的应用。为了开始建立有效的自主监测方法，开发了一个仿真（FLAME）用于算法测试。为了模拟野火，使用了成熟的FARSITE火灾模拟器来生成真实的火灾行为模型。FARSITE是一个野火模拟器，事故指挥官（IC）在现场使用它来预测火灾的蔓延，使用地形、天气、风、湿度和燃料数据。从FARSITE获得的数据被导入FLAME并解析为动态边界，用于测试热点监测算法。在本文中，沿着边界的兴趣点被建立为火线强度（英国-热-单位/英尺/秒）高于设定阈值的点。使用Mini-Batch K-means聚类技术将这些兴趣点细化为热点。用距离阈值区分移动热点中心和新开发热点。将提出的算法与基线进行比较，以最小化未跟踪的最大时间J(t)的总和。结果表明，简单地绕火圈的性能很差（基线），而加权贪婪度量（建议）的性能明显更好。该算法随后在一架无人机上运行，以验证现实世界实现的可行性。
This paper explores the use of unmanned aerial vehicles (UAVs) in wildfire monitoring. To begin establishing effective methods for autonomous monitoring, a simulation (FLAME) is developed for algorithm testing. To simulate a wildfire, the well established FARSITE fire simulator is used to generate realistic fire behavior models. FARSITE is a wildfire simulator that is used in the field by Incident Commanders (IC’s) to predict the spread of the fire using topography, weather, wind, moisture, and fuel data. The data obtained from FARSITE is imported into FLAME and parsed into a dynamic frontier used for testing hotspot monitoring algorithms. In this paper, points of interest along the frontier are established as points with a fireline intensity (British-Thermal-Unit/feet/second) above a set threshold. These interest points are refined into hotspots using the Mini-Batch K-means Clustering technique. A distance threshold differentiates moving hotspot centers and newly developed hotspots. The proposed algorithm is compared to a baseline for minimizing the sum of the max time untracked J(t). The results show that simply circling the fire performs poorly (baseline), while a weighted-greedy metric (proposed) performs significantly better. The algorithm was then run on a UAV to demonstrate the feasibility of real world implementation.