Multi-objective evolutionary approach for UAV cruise route planning to collect traffic information
来源期刊：中南大学学报(英文版)2012年第12期
论文作者：LIU Xiao-feng(刘晓锋) PENG Zhong-ren(彭仲仁) CHANG Yun-tao(常云涛) ZHANG Li-ye(张立业)
文章页码：3614 - 3621
Key words：traffic information collection; unmanned aerial vehicle; cruise route planning; multi-objective optimization
Abstract: Unmanned aerial vehicle (UAV) was introduced as a novel traffic device to collect road traffic information and its cruise route planning problem was considered. Firstly, a multi-objective optimization model was proposed aiming at minimizing the total cruise distance and the number of UAVs used, which used UAV maximum cruise distance, the number of UAVs available and time window of each monitored target as constraints. Then, a novel multi-objective evolutionary algorithm was proposed. Next, a case study with three time window scenarios was implemented. the results show that both the total cruise distance and the number of UAVs used continue to increase with the time window constraint becoming narrower. Compared with the initial optimal solutions, the optimal total cruise distance and the number of UAVs used fall by an average of 30.93% and 31.74%, respectively. Finally, some concerns using UAV to collect road traffic information were discussed.
LIU Xiao-feng(刘晓锋), PENG Zhong-ren(彭仲仁), CHANG Yun-tao(常云涛), ZHANG Li-ye(张立业)
(School of Transportation Engineering, Tongji University, Shanghai 201804, China)
Abstract:Unmanned aerial vehicle (UAV) was introduced as a novel traffic device to collect road traffic information and its cruise route planning problem was considered. Firstly, a multi-objective optimization model was proposed aiming at minimizing the total cruise distance and the number of UAVs used, which used UAV maximum cruise distance, the number of UAVs available and time window of each monitored target as constraints. Then, a novel multi-objective evolutionary algorithm was proposed. Next, a case study with three time window scenarios was implemented. the results show that both the total cruise distance and the number of UAVs used continue to increase with the time window constraint becoming narrower. Compared with the initial optimal solutions, the optimal total cruise distance and the number of UAVs used fall by an average of 30.93% and 31.74%, respectively. Finally, some concerns using UAV to collect road traffic information were discussed.
Key words:traffic information collection; unmanned aerial vehicle; cruise route planning; multi-objective optimization
