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Considering both energy effectiveness and flight safety in UAV trajectory planning for intelligent logistics
Vehicular Communications ( IF 5.8 ) Pub Date : 2025-01-17 , DOI: 10.1016/j.vehcom.2025.100885
Zhiyang Liu, Liuhuan Li, Xiao Zhang, Wan Tang, Zhen Yang, Ximin Yang
Vehicular Communications ( IF 5.8 ) Pub Date : 2025-01-17 , DOI: 10.1016/j.vehcom.2025.100885
Zhiyang Liu, Liuhuan Li, Xiao Zhang, Wan Tang, Zhen Yang, Ximin Yang
In low-altitude economic logistics scenarios, trajectory planning for unmanned aerial vehicles (UAVs) can be treated as a typical traveling salesman problem (TSP). High-rise buildings in urban areas not only severely impact the flight safety of UAVs, but also increase their energy consumption when avoiding obstacles, thereby affecting their delivery ranges. To address these issues, this paper proposes a two-stage trajectory planning solution called ACO-DQN-TP for logistics UAVs. In the first stage, the ant colony optimization (ACO) algorithm is applied to solve the sequence for multi-target point deliveries, to obtain the optimal flight paths. The ant tabu table is reopened to allow for retracing of the movement paths in order to avoid forward search dilemmas. In the second stage, a deep Q-network (DQN) is combined with the traditional artificial potential field method to enhance the interaction between UAVs and their environment. The rewards are accumulated using two potential functions generated based on the target points and obstacles, to minimize the changes in the yaw angles and smooth the flight trajectory of the UAV. Simulation experiments were conducted on UAV trajectory planning for delivery missions with four to ten target points. The simulation results show that the average path length obtained by ACO-DQN-TP is 65% and 79% shorter than that of Greedy+DQNPF and BACO, respectively, and the sum of turning angles along the path is 56% of Greedy DQNPF and 72% of BACO on average. It indicates that the proposed ACO-DQN-TP scheme not only optimizes delivery routes compared to traditional ACOs but also effectively controls the magnitude of the changes in heading angle during flight. This ensures flight safety for the UAV through obstacle avoidance while reducing flight energy consumption. In particular, the heading angle optimization mechanism proposed in this paper has universal guiding significance for low-altitude flights in the areas of traffic and transportation using UAVs.
中文翻译:
智能物流无人机轨迹规划中兼顾能源效率和飞行安全
在低空经济物流场景中,无人机 (UAV) 的轨迹规划可以被视为典型的旅行推销员问题 (TSP)。城市地区的高层建筑不仅严重影响无人机的飞行安全,而且在避障时会增加其能耗,从而影响其发射距离。为了解决这些问题,本文提出了一种用于物流无人机的两阶段轨迹规划解决方案,称为 ACO-DQN-TP。在第一阶段,应用蚁群优化 (ACO) 算法求解多目标点投递的顺序,以获得最优飞行路径。重新打开 ant tabu 表以允许回溯移动路径,以避免前向搜索困境。在第二阶段,深度 Q 网络 (DQN) 与传统的人工势场方法相结合,以增强无人机与其环境之间的交互。奖励是使用基于目标点和障碍物生成的两个潜在函数累积的,以最大限度地减少偏航角的变化并平滑无人机的飞行轨迹。对具有 4 到 10 个目标点的交付任务的无人机轨迹规划进行了模拟实验。仿真结果表明,ACO-DQN-TP获得的平均路径长度分别比Greedy+DQNPF和BACO短65%和79%,沿路径的转弯角度之和平均为Greedy DQNPF的56%和BACO的72%。这表明,与传统 ACO 相比,所提出的 ACO-DQN-TP 方案不仅优化了投递路线,而且有效地控制了飞行过程中航向角变化的幅度。 这通过避障确保了无人机的飞行安全,同时降低了飞行能耗。特别是,本文提出的航向角优化机制对于使用无人机的交通运输领域的低空飞行具有普遍的指导意义。
更新日期:2025-01-17
中文翻译:
![](https://scdn.x-mol.com/jcss/images/paperTranslation.png)
智能物流无人机轨迹规划中兼顾能源效率和飞行安全
在低空经济物流场景中,无人机 (UAV) 的轨迹规划可以被视为典型的旅行推销员问题 (TSP)。城市地区的高层建筑不仅严重影响无人机的飞行安全,而且在避障时会增加其能耗,从而影响其发射距离。为了解决这些问题,本文提出了一种用于物流无人机的两阶段轨迹规划解决方案,称为 ACO-DQN-TP。在第一阶段,应用蚁群优化 (ACO) 算法求解多目标点投递的顺序,以获得最优飞行路径。重新打开 ant tabu 表以允许回溯移动路径,以避免前向搜索困境。在第二阶段,深度 Q 网络 (DQN) 与传统的人工势场方法相结合,以增强无人机与其环境之间的交互。奖励是使用基于目标点和障碍物生成的两个潜在函数累积的,以最大限度地减少偏航角的变化并平滑无人机的飞行轨迹。对具有 4 到 10 个目标点的交付任务的无人机轨迹规划进行了模拟实验。仿真结果表明,ACO-DQN-TP获得的平均路径长度分别比Greedy+DQNPF和BACO短65%和79%,沿路径的转弯角度之和平均为Greedy DQNPF的56%和BACO的72%。这表明,与传统 ACO 相比,所提出的 ACO-DQN-TP 方案不仅优化了投递路线,而且有效地控制了飞行过程中航向角变化的幅度。 这通过避障确保了无人机的飞行安全,同时降低了飞行能耗。特别是,本文提出的航向角优化机制对于使用无人机的交通运输领域的低空飞行具有普遍的指导意义。