优先地图用于监视和干预野火及其他扩散过程
Priority Maps for Surveillance and Intervention of Wildfires and other Spreading Processes
AI总结：
本文提出了一种生成优先地图的方法，用于监视或干预动态扩散过程，如野火。该方法利用正系统性质，特别是价值函数的分离结构，提供可扩展的算法。通过16和1000节点示例展示了方法如何响应系统动态变化，并结合旅行商问题进行无人机路径规划。
摘要：
无人驾驶航空器（UAV）路径规划算法通常假设一个知识奖励函数或优先地图，指示最重要的区域。本文提出了一种方法，用于生成监视或干预动态扩散过程（如野火）的优先地图。所提出的优化框架利用正系统的性质，特别是价值函数（成本到目标）的分离结构，提供可扩展的监视和干预算法。我们展示了16和1000节点示例的结果，并说明了优先地图如何响应系统动态的变化。1000节点的更大示例代表一个虚构景观，展示了该方法如何整合野火扩散动态、景观和风条件。最后，我们给出将所提方法与旅行商问题结合用于野火干预的无人机路径规划示例。
英文摘要：
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.
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