面向低空快递配送的空地协同路径优化研究

Research on Air-ground Cooperative Path Optimization for Low-altitude Express Delivery

无人机与地面车辆联合运输是解决快递配送“最后一公里”问题的有效手段,然而无人机电池能量的有限性等因素对实现空地联运提出新的挑战。为此,提出了综合考虑地理位置及无人机能耗约束的启发式空地协同路径优化方法。首先,建立多参数耦合的无人机能耗模型,进而考虑无人机释放点位置、无人机能耗因素设计约束条件,提出包含整数决策变量的空地协同路径优化模型;然后,通过约束解耦,提出了融合粒子群优化机制的两阶段启发式算法。最后,与传统旅行商算法、模拟退火算法的对比实验结果表明,所提出的算法能够使平均配送成本分别降低12%和3%,进一步验证了所提算法的收敛性和有效性。

Addressing the"last-kilometre"delivery challenge,unmanned aerial vehicles(UAVs)and ground vehicles collaborate effectively.However,limited UAV battery energy presents new challenges for air-ground cooperation.Hence,this paper proposes a heuristic air-ground path optimization method,comprehensively considering geography and UAV energy constraints.Firstly,a UAV energy model with multiple parameters is established.Then,constraints including UAV release locations and energy consumption are designed to form an optimization model with integer decision variables.Next,a Two-Stage Heuristic algorithm integrating particle swarm optimization is proposed by decoupling constraints.Finally,comparative experimental results with traditional Traveling Salesman Problem algorithm and Fixed-Range Simulated Annealing algorithm demonstrate that the proposed algorithm can reduce average delivery costs by 12%and 3%,respectively verifying the convergence and effectiveness of the algorithm.