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Two-agent cooperative search using game models with endurance-time constraints

机译：使用具有持续时间约束的博弈模型进行两主体协作搜索

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In this article, the problem of two Unmanned Aerial Vehicles (UAVs) cooperatively searching an unknown region is addressed. The search region is discretized into hexagonal cells and each cell is assumed to possess an uncertainty value. The UAVs have to cooperatively search these cells taking limited endurance, sensor and communication range constraints into account. Due to limited endurance, the UAVs need to return to the base station for refuelling and also need to select a base station when multiple base stations are present. This article proposes a route planning algorithm that takes endurance time constraints into account and uses game theoretical strategies to reduce the uncertainty. The route planning algorithm selects only those cells that ensure the agent will return to any one of the available bases. A set of paths are formed using these cells which the game theoretical strategies use to select a path that yields maximum uncertainty reduction. We explore non-cooperative Nash, cooperative and security strategies from game theory to enhance the search effectiveness. Monte-Carlo simulations are carried out which show the superiority of the game theoretical strategies over greedy strategy for different look ahead step length paths. Within the game theoretical strategies, non-cooperative Nash and cooperative strategy perform similarly in an ideal case, but Nash strategy performs better than the cooperative strategy when the perceived information is different. We also propose a heuristic based on partitioning of the search space into sectors to reduce computational overhead without performance degradation.
机译：在本文中，解决了两个无人飞行器（UAV）协同搜索未知区域的问题。将搜索区域离散为六边形单元，并假定每个单元都具有不确定性值。无人飞行器必须在考虑有限的耐力，传感器和通信范围约束的情况下合作搜索这些小区。由于有限的耐久性，无人飞行器需要返回基站进行加油，并且当存在多个基站时也需要选择一个基站。本文提出了一种路线规划算法，该算法考虑了耐力时间约束，并使用博弈论策略来减少不确定性。路由计划算法仅选择那些确保代理将返回到任何可用基准的单元。使用这些单元形成了一组路径，游戏理论策略使用这些路径来选择可最大程度减少不确定性的路径。我们从博弈论探讨非合作Nash，合作和安全策略，以提高搜索效率。进行了蒙特卡洛模拟，显示了对于不同的前瞻性步长路径，博弈理论策略比贪婪策略的优越性。在博弈论策略中，非合作Nash和合作策略在理想情况下的表现相似，但是当感知信息不同时，Nash策略的表现要好于合作策略。我们还提出了一种基于将搜索空间划分为多个扇区的启发式方法，以减少计算开销而不会降低性能。

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