一种在噪声限制区域中飞行的混合无人机路径规划算法
A Path Planning Algorithm for a Hybrid UAV Traveling in Noise Restricted Zones
AI总结：
针对混合动力无人机在噪声限制区域中的路径规划问题，提出一种基于混合整数凸规划的方法，通过参数化静音区边界点将问题转化为凸集图上的最短路径，并扩展至TSP，计算效率大幅提升且成本接近最优。
摘要：
本文提出了一种集成方法，用于配备双燃料-电力推进系统的混合动力无人机（HUAV）的高效路径规划与能量管理。这些HUAV在包含噪声限制区域（称为静音区）的环境中运行，在静音区内仅允许使用电动模式。我们通过使用其端点和标量参数对静音区边界上一点的位置进行参数化，将该问题转化为在凸集图上寻找最短路径的变体。我们将此问题表述为混合整数凸规划（MICP），该问题可使用商业求解器高效求解。此外，通过松弛路径选择变量，可以获得一个紧的下界。通过在四个地图上的200个实例进行大量计算，我们展示了相对于最先进方法的计算效率显著提升，计算下界和精确解的时间分别减少了高达100倍和10倍。此外，精确成本与下界之间的平均差距约为0.24%，且精确成本平均比最先进方法的可行解低1.05%，突显了我们方法的有效性。我们还将我们的方法扩展到在具有静音区的环境中规划HUAV访问一组目标并返回起始位置的路线，从而产生一个旅行商问题（TSP）。我们采用两种方法求解TSP：一种是对每个目标处的SOC进行离散化，另一种是假设出发时SOC为最小允许水平。比较分析显示，第二种方法平均成本在第一种方法的1.02%以内，同时所需计算时间显著减少。
英文摘要：
This paper presents an integrated approach for efficient path planning and energy management in hybrid unmanned aerial vehicles (HUAVs) equipped with dual fuel-electric propulsion systems. These HUAVs operate in environments that include noise-restricted zones, referred to as quiet zones, where only electric mode is permitted. We address the problem by parameterizing the position of a point along the side of the quiet zone using its endpoints and a scalar parameter, transforming the problem into a variant of finding the shortest path over a graph of convex sets. We formulate this problem as a mixed-integer convex program (MICP), which can be efficiently solved using commercial solvers. Additionally, a tight lower bound can be obtained by relaxing the path-selection variable. Through extensive computations across 200 instances over four maps, we show a substantial improvement in computational efficiency over a state-of-the-art method, achieving up to a 100-fold and 10-fold decrease in computation time for calculating the lower bound and the exact solution, respectively. Moreover, the average gap between the exact cost and the lower bound was approximately 0.24%, and the exact cost was 1.05% lower than the feasible solution from the state-of-the-art approach on average, highlighting the effectiveness of our method. We also extend our approach to plan the HUAV route to visit a set of targets and return to its starting location in environments with quiet zones, yielding a Traveling Salesman Problem (TSP). We employ two methodologies to solve the TSP: one where the SOC at each target is discretized, and another where it is assumed to be the minimum allowable level upon departure. A comparative analysis reveals the second method achieves a cost within 1.02% of the first on average while requiring significantly less computational time.
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