Unmanned Aerial Vehicle Route Planning for Traffic Information Collection

In this paper, the unmanned aerial vehicle (UAV) route planning problem is introduced to deploy the UAV for road traffic information collection. The scenario of using limited UAVs to detect road sections is considered, and a multi-objective optimization model is developed, which uses the number of the UAVs and UAV maximum cruise distance as constraints and aims to minimize the total cruise distance and maximize the number of detected road sections. A novel non-dominated sorting genetic algorithm for this problem is then proposed. The case study shows that the nearly optimal solution for planning UAV routes can be acquired effectively. Compared the obtained solution with the optimal feasible solution, the total cruise distance is reduced by 13.07% and the number of detected targets is increased by 41.67%. Finally, some issues on deploying UAVs for traffic information collection are discussed.

摘要引入无人飞机作为城市道路固定交通检测设备的辅助手段，部署无人飞机进行道路交通信息采集，提出了无人飞机的路径规划问题。考虑了无人飞机数量有限，不足以对所有目标进行侦察的情形，建立了以总巡航距离最短、巡航目标数量最多的多目标优化模型，提出了可行路径的重组方法，构造了求解该问题的非支配排序遗传算法。案例分析结果表明：构造的算法可以求出无人飞机路径规划的近似最优解，与最优初始可行解相比，总巡航距离减少了13.07%，巡航目标数量增加了41.67%。最后，讨论了无人飞机在道路交通信息采集中可能面临的问题。