Abstract
Unmanned Aerial Vehicle (UAV) path planning algorithms often assume a knowledge reward function or priority map, indicating the most important areas to visit. In this paper we propose a method to create priority maps for monitoring or intervention of dynamic spreading processes such as wildfires. The presented optimization framework utilizes the properties of positive systems, in particular the separable structure of value (cost-to-go) functions, to provide scalable algorithms for surveillance and intervention. We present results obtained for a 16 and 1000 node example and convey how the priority map responds to changes in the dynamics of the system. The larger example of 1000 nodes, representing a fictional landscape, shows how the method can integrate bushfire spreading dynamics, landscape and wind conditions. Finally, we give an example of combining the proposed method with a travelling salesman problem for UAV path planning for wildfire intervention.
Abstract (translated)
无人机路径规划算法通常采用知识奖励函数或优先权图，表示最重要的访问区域。在本文中，我们提出了一种创建优先级图的方法，用于监测或干预动态蔓延过程，如野火。所提出的优化框架利用了正系统的特性，特别是价值（成本）函数的可分离结构，为监控和干预提供了可扩展的算法。我们给出了一个16节点和1000节点示例的结果，并说明优先级映射如何响应系统动态变化。更大的1000个节点的例子，代表了一个虚构的景观，展示了该方法如何整合丛林火蔓延动力学，景观和风的条件。最后给出了一个将该方法与旅行商问题相结合的无人机野火干涉路径规划的实例。
URL
https://arxiv.org/abs/1903.11204
