基于完善交通信息收集的UAV路径规划

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交通运输研究 ›› 2018, Vol. 4 ›› Issue (5) : 57-62.

战略与政策

基于完善交通信息收集的UAV路径规划

王冬冬,何胜学,路扬

作者信息 +

UAV Path Planning Based on Perfect Traffic Information Collection

WANG Dong-dong, HE Sheng-xue and LU Yang

Author information +

文章历史 +

摘要

针对部分路段不能获取完整交通信息的问题，提出使用无人机对未布设固定型交通信息检测器路段进行交通巡视，完善交通信息。通过时空网络建立了一个总飞行时间最短、最大单机飞行时间最短的多目标模型，确定最佳的无人机数量和交通信息收集路径。新模型不仅利用时空网络技术细致刻画了无人机在巡视过程中的飞行轨迹，而且加入了对未布设固定型交通检测器路段的巡视次数以及巡视时间间隔约束问题，使巡视路径更加合理。情景分析表明，使用两架无人机进行巡视时，总飞行时间最短为37min，在23min 内完成巡视任务；随着最大单机飞行时间权重的增大，无人机的总飞行时间增加9.76%，最大单机飞行时间减少8.70%。算例分析表明，所建模型和方法能够解决大规模路网的多无人机调度问题，能够根据实际需求得到满意的巡视路径。

Abstract

In order to solve the problem that some road segments cannot obtain complete traffic information, and to perfect traffic information, UAV(Unmanned Aerial Vehicle) was proposed to conduct traffic inspections on unfixed traffic information detector road sections. Based on space-time network, a multi-objective model with the minimum total flight time and the minimum single flight time was established, and the optimal number of UAVs and traffic information collection path were determined. The new model not only made use of the space-time network technology to meticulously depict the flight trajectory of the UAV in the process of inspection, but also added the times of patrol and restrictions on the patrol time interval for the unfixed traffic detector sections, which made the patrol route more reasonable. Scenario analysis showed that when using two UAVs for patrol, the total flight time was as short as 37 minutes, and the patrol mission was completed within 23 minutes. As the maximum single UAV flight time weight increasing, the total flight time of the UAV increased by 9.76%, and the maximum single flight time reduced by 8.70%. The example shows that the proposed model and method can solve the multi-UAV scheduling problem of large-scale road network, and can obtain a satisfactory patrol path according to actual needs.

导出引用

王冬冬,何胜学,路扬. 基于完善交通信息收集的UAV路径规划[J]. 交通运输研究. 2018, 4(5): 57-62

WANG Dong-dong, HE Sheng-xue and LU Yang. UAV Path Planning Based on Perfect Traffic Information Collection[J]. Transport Research. 2018, 4(5): 57-62

参考文献

[1] 曹德胜，桑凌志. 无人机水上遇险搜救关键技术研究[J]. 交通运输研究，2017，3（3）：62-68.
[2] 彭向阳，陈驰，饶章权，等. 基于无人机多传感器数据采集的电力线路安全巡检及智能诊断[J]. 高电压技术，2015，41（1）：159-166.
[3] 彭向阳，刘正军，麦晓明，等. 无人机电力线路安全巡检系统及关键技术[J]. 遥感信息，2015，30（1）：51-57.
[4] 王柯，彭向阳，陈锐民，等. 无人机电力线路巡视平台选型[J]. 电力科学与工程，2014，30（6）：46-53.
[5] 程涛，翁松伟，陈海雄，等. 一种基于小型无人机的道路交通巡检系统及其巡检方法[P]. 中国专利：104332053A, 2015-02-04.
[6] KIM N V, CHERVONENKIS M A. Situation Control of Unmanned Aerial Vehicles for Road Traffic Monitoring[J]. Modern Applied Science, 2015, 9(5): 1-13.
[7] KARAKAYA M. UAV Route Planning for Maximum Target Coverage[J]. Computer Science & Engineering: An International Journal, 2014, 4(1): 27-34.
[8] HUANG L W, QU H, JI P, et al. A Novel Coordinated Path Planning Method Using K - degree Smoothing For Multi-UAVs[J]. Applied Soft Computing, 2016, 48: 182-192.
[9] AVELLAR G S C, PEREIRA G A S, PIMENTA L C A, et al. Multi- UAV Routing for Area Coverage and Remote Sensing with Minimum Time[J].Sensors,2015,15(11):27783-27803.
[10] NIU S Y, ZHANG J S, ZHANG F, et al. A Method of UAVs Route Optimization Based on the Structure of the Highway Network[J]. International Journal of Distributed Sensor Networks, 2015, 2015(1): 1667-1670.
[11] 刘晓锋，彭仲仁，张立业，等. 面向交通信息采集的无人飞机路径规划[J]. 交通运输系统工程与信息，2012，12（1）：91-97.
[12] 刘晓锋，常云涛，王珣. 稀疏路网条件下的无人飞机交通监控部署方法[J]. 公路交通科技， 2012，29（3）：128-134.
[13] LIU X F, PENG Z R, CHANG Y T, et al. Multi-objective Evolutionary Approach for UAV Cruise Route Planning to Collect Traffic Information[J]. Journal of Central South University, 2012, 19(12): 3614-3621.
[14] 何胜学，陈经纬. 考虑上客点选择的公交紧急疏散时空路网模型[J]. 计算机应用研究，2017，34（7）：2001-2005.
[15] 陈经纬，何胜学，程龙. 基于时空网络的无人车运行计划制定[J]. 计算机应用研究，2017，34（12）：3642-3646.
[16] 管琳，白艳萍. 用分支定界算法求解旅行商问题[J]. 中北大学学报（自然科学版），2007（2）：104-107.
[17] 甘宝，薛玉玺，魏文萍. 基于改进遗传算法的车辆路径问题[J]. 交通运输研究，2015，1（4）：88-94.
[18] 刘雪萍，朱海洋，韩瑜睿，等. 应急救援物资多式联运的成本最优化模型[J]. 交通运输研究，2016，2（1）：71-76.