Groups of battery powered unmanned aerial vehicles (UAVs) are effective in a variety of scenarios that require autonomous cooperation to achieve a goal. However, the complexity of modeling and analyzing UAV cooperation in situations with stochastic elements leads to unique challenges. This paper introduces a framework for one such problem domain, the multi-UAV persistent search and retrieval task with stochastic target appearances (PSR-STA), in which UAVs continuously search an area for stochastically appearing targets to retrieve and deliver them to collector locations. Design decisions are introduced for understanding how to successfully simulate multi-UAV PSR-STA. Common tools for analyzing search algorithm effectiveness through statistical and graphical methods are presented. A case study of multi-UAV park cleanup is implemented to demonstrate the framework, where algorithms for choosing the locations of collectors and charging stations based on stochastic target appearance models are proposed, methods for continuous multi-UAV operation over a long period time are demonstrated, and the differences in effectiveness between four coverage search patterns are analyzed.

电池供电的无人机 (UAV) 组在需要自主合作以实现目标的各种场景中都很有效。然而，在具有随机元素的情况下建模和分析无人机合作的复杂性导致了独特的挑战。本文介绍了一个针对此类问题域的框架，即具有随机目标外观的多无人机持久搜索和检索任务（PSR-STA），其中无人机不断搜索一个区域以寻找随机出现的目标，以检索并将它们传送到收集器位置。引入设计决策以了解如何成功模拟多无人机 PSR-STA。介绍了通过统计和图形方法分析搜索算法有效性的常用工具。