This paper presents a system to command and control a team of fixed-wing unmanned aerial vehicles (UAVs) to sense dynamic wildfire boundaries. UAV team task and trajectory planning strategies enable the team to rapidly find, rally around, and map the wildfire boundaries. A novel boundary estimation algorithm generates two-dimensional concave polygonal estimates of multiple dynamic boundaries given sparse observation data. The algorithm was tested with simulated wildfire scenario binary fire or free-point observations collected by the UAV team. First, all gathered observations are used to classify groups of points into clusters belonging to individual wildfires; then, spatiotemporal information from wildfire observations is encoded as an image with observation age represented as pixel brightness. A neural network performs semantic segmentation on each image and outputs a predicted binary image of the wildfire. This image is decoded back into a point set that feeds into a boundary estimation algorithm (Polylidar) to extract a concave boundary. Benchmarks for planner and boundary estimation times and accuracy comparisons are provided. Our boundary estimation algorithm and supporting multiagent planning strategies were used to win the 2019 U.S. Air Force Research Laboratory’s Swarm and Search Wildfire challenge using the aerospace multiagent simulation environment.

本文提出了一种命令和控制一组固定翼无人飞行器（UAV）的系统，以感知动态野火边界。无人机团队的任务和弹道规划策略使团队能够快速查找，聚集并绘制野火边界。给定稀疏的观测数据，一种新颖的边界估计算法可生成多个动态边界的二维凹面多边形估计。该算法已通过模拟野火场景的二进制火或无人机点收集的自由点观测值进行了测试。首先，使用所有收集到的观测值将点组划分为属于单个野火的群集；然后，将野火观测的时空信息编码为图像，并将观测年龄表示为像素亮度。神经网络在每个图像上执行语义分割，并输出野火的预测二进制图像。该图像被解码回一个点集，该点集输入边界估计算法（Polylidar）以提取凹形边界。提供了计划者和边界估计时间以及准确性比较的基准。我们的边界估计算法和支持的多主体规划策略用于利用航空多主体仿真环境赢得2019年美国空军研究实验室的Swarm and Search Wildfire挑战。提供了计划者和边界估计时间以及准确性比较的基准。我们的边界估计算法和支持的多主体规划策略用于利用航空多主体仿真环境赢得2019年美国空军研究实验室的Swarm and Search Wildfire挑战。提供了计划者和边界估计时间以及准确性比较的基准。我们的边界估计算法和支持的多主体规划策略用于利用航空多主体仿真环境赢得2019年美国空军研究实验室的Swarm and Search Wildfire挑战。