SKYSURF：一种基于合作空中滑翔机的自学习框架用于持久监视
SKYSURF: A Self-learning Framework for Persistent Surveillance using Cooperative Aerial Gliders
AI总结：
SKYSURF提出了一种自学习框架，通过合作空中滑翔机实现持久监视，通过局部-全局行为管理提升路径规划和碰撞避免，减少能耗并提高目标检测效率。
摘要：
基于小型无人驾驶航空器（UAV）的监视应用的成功取决于其有限的机载电源能持续多长时间。为应对这一挑战，寻求替代的可再生能源来源。一种有前途的解决方案是利用上升的浮力空气提取能量。本文提出了一种局部-全局行为管理和决策方法，用于自主部署具有升力能力的UAV。合作UAV被建模为非确定性有限状态的理性代理。除了一个任务规划模块，用于分配任务并发布动态导航航点，以实现新的路径规划方案，其中应用了可见性和预测的概念以避免碰撞。此外，采用延迟学习和调整策略来优化路径跟踪控制器的增益。与三个基准基线和15种进化算法进行的严格比较分析突显了所提出方法在维持监视持续性（无需着陆长时间飞行）和最大化目标检测（比非合作和半合作方法好两倍）以及减少能耗（几乎消耗了六小时电池的6%）方面的充分性。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
