The success of surveillance applications involving small unmanned aerial vehicles (UAVs) depends on how long the limited on-board power would persist. To cope with this challenge, alternative renewable sources of lift are sought. One promising solution is to extract energy from rising masses of buoyant air. This paper proposes a local-global behavioral management and decision-making approach for the autonomous deployment of soaring-capable UAVs. The cooperative UAVs are modeled as non-deterministic finite state-based rational agents. In addition to a mission planning module for assigning tasks and issuing dynamic navigation waypoints for a new path planning scheme, in which the concepts of visibility and prediction are applied to avoid the collisions. Moreover, a delayed learning and tuning strategy is employed optimize the gains of the path tracking controller. Rigorous comparative analyses carried out with three benchmarking baselines and 15 evolutionary algorithms highlight the adequacy of the proposed approach for maintaining the surveillance persistency (staying aloft for longer periods without landing) and maximizing the detection of targets (two times better than non-cooperative and semi-cooperative approaches) with less power consumption (almost 6% of battery consumed in six hours).

翻译：涉及小型无人机（UAV）的监视应用能否成功，取决于有限的机载能源能持续多久。为应对这一挑战，人们寻求替代性的可再生升力来源。一种有前景的解决方案是从上升的浮力空气中提取能量。本文提出了一种局部-全局行为管理与决策方法，用于自主部署具备翱翔能力的无人机。协作无人机被建模为基于非确定性有限状态的理性智能体。除了一个用于分配任务并为新的路径规划方案发布动态导航航点的任务规划模块外，该方案还应用了可见性与预测概念以避免碰撞。此外，采用了一种延迟学习与调谐策略来优化路径跟踪控制器的增益。通过与三个基准测试基线和15种进化算法进行的严格比较分析，突显了所提方法在维持监视持久性（更长时间保持飞行而不着陆）和最大化目标检测（比非协作和半协作方法好两倍）方面的充分性，同时功耗更低（六小时内仅消耗约6%的电池电量）。