发表机构
University of Minnesota; Cornell University; DEVCOM Army Research Laboratory（明尼苏达大学; 康奈尔大学; 美国陆军研究实验室DEVCOM）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对移动充电支持的UAV，提出平滑非线性轨迹优化模型，耦合任务调度与充电决策，通过有界误差平滑近似析取约束，避免整数变量，大幅降低计算时间。
AI 中文摘要
为无人飞行器（UAV）配备移动充电站，能够在基础设施稀疏的环境中实现UAV的持续自主运行。在此背景下，UAV的轨迹优化具有挑战性，因为它将任务调度与何时何地充电相耦合，同时还涉及充电可用地点的地形可达性约束。我们提出了一种平滑的非线性轨迹优化模型，用于具有移动充电支持的UAV。与现有结果相比，所提出的模型通过统一的电池动力学模型以及针对每种模式分配时间的析取约束，允许非线性充电动力学模式。此外，它提供了具有有界逼近误差的析取约束的平滑近似。通过避免整数变量，这些近似使得能够使用平滑非线性优化算法进行高效求解。我们在具有多个空间分布任务、非线性恒流-恒压充电动力学以及移动充电支持的地形可达性约束的UAV任务上评估了所提出的模型。与混合整数非线性规划相比，所提出的模型提供了高质量的近似解，同时将计算时间减少了几个数量级。
英文摘要
Supporting Uncrewed Aerial Vehicles (UAVs) with mobile charging stations enables persistent UAV autonomy in infrastructure-sparse environments. In this setting, trajectory optimization for UAVs is challenging because it couples task scheduling with when and where to recharge, as well as terrain-access constraints on where charging is available. We propose a smooth nonlinear trajectory optimization model for UAV with mobile charging support. Compared with existing results, the proposed model allows nonlinear charging dynamics mode via a unified battery dynamics model with disjunctive constraints on the time allocated to each mode. Furthermore, it provides smooth approximations of the disjunctive constraints with bounded approximation errors. By avoiding integer variables, these approximations enable efficient solution using smooth nonlinear optimization algorithms. We evaluate the proposed model on UAV missions with multiple spatially distributed tasks, nonlinear constant-current--constant-voltage charging dynamics, and terrain-access constraints on mobile charging support. Compared with mixed-integer nonlinear programs, the proposed model provides high-quality approximate solutions while reducing the computation time by orders of magnitude.
