摘 要：针对城市低空复杂环境下物流无人机任务分配面临的多因素耦合制约、运行风险频发、运输效率低下、时空成本高昂等难题，提出了城市低空物流任务协同动态分配方法。首先，采用数字高程模型与栅格法构建城市低空空域静态、动态环境模型，在此基础上，针对城市低空物流无人机飞行导致的人身安全、噪声污染、财产损失与隐私泄露等第三方社会风险进行精细评价；其次，综合考虑避障、物流任务时效性、飞行性能等因素，提出低空物流任务协同动态分配双层耦合模型，上层模型以运输成本最小为目标协同优化任务分配，下层以第三方社会风险最低为目标动态规划航迹，双层模型相互耦合、嵌套调用；最后，提出遗传算法全局搜索-粒子群算法局部优化融合算法，并设计改进A*算法，以获得最佳分配方案。实例分析表明，本文所提方法得到的低空物流任务协同动态分配结果在爬升时间、下降时间、风险、飞行距离和飞行总时间上均有改进，相较于传统方法分别平均优化了19.60%、23.44%、19.00%、22.90%和22.64%，同时，通过异构无人机协同实验与多任务规模梯度实验验证了该方法具有良好的适用性，可为城市低空物流高效、安全运行提供有力技术支撑。
Abstract: Aiming at the problems of multi-factor coupling constraints, frequent operation risks, low transportation efficiency, and high spatio-temporal costs faced by the task allocation of logistics drones in urban low-altitude complex environments, a collaborative dynamic task allocation method for urban low-altitude logistics is proposed. Firstly, a static and dynamic environment model of urban low-altitude airspace is constructed by using digital elevation models and the grid method. On this basis, a fine evaluation of third-party social risks such as personal safety, noise pollution, property damage, and privacy leakage caused by the flight of urban low-altitude logistics drones is conducted. Secondly, considering factors such as obstacle avoidance, the timeliness of logistics tasks, and flight performance, a two-layer coupled model for collaborative dynamic task allocation of low-altitude logistics is proposed. The upper layer model aims to minimize transportation costs and optimizes task allocation collaboratively, while the lower layer aims to minimize third-party social risks and dynamically plans flight paths. The two layers are coupled and nested. Finally, a genetic algorithm for global search and particle swarm optimization for local optimization are fused, and an improved A* algorithm is designed to obtain the best allocation scheme. The case analysis shows that the collaborative dynamic task allocation results of urban low-altitude logistics obtained by the proposed method have improvements in climb time, descent time, risk, flight distance, and total flight time. Compared with traditional methods, they are optimized by an average of 19.60%, 23.44%, 19.00%, 22.90%, and 22.64%, respectively. Meanwhile, the applicability of the method is verified through heterogeneous drone collaborative experiments and multi-task scale gradient experiments, which can provide strong technical support for the efficient and safe operation of urban low-altitude logistics.
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