车辆与无人机联合配送模式在产业界受到青睐,该模式有效地降低了配送成本,但却有极大的调度难度,问题的求解也非常复杂。本文对问题进行明确定义并建立模型,根据问题特性设计了一个自适应大规模邻域搜索(Adaptive Large Neighborhood Se...车辆与无人机联合配送模式在产业界受到青睐,该模式有效地降低了配送成本,但却有极大的调度难度,问题的求解也非常复杂。本文对问题进行明确定义并建立模型,根据问题特性设计了一个自适应大规模邻域搜索(Adaptive Large Neighborhood Search,ALNS)算法,进行了大量的实验的对比和分析。研究结果表明,ALNS算法相比Gurobi在运行时间上有明显优势,结果相同甚至更优;车辆与无人机联合配送模式也较仅卡车配送模式节约了成本。展开更多
针对物流配送需求大、“最后一公里”交付困难等问题,提出带有动态能耗约束的多车辆与多无人机协同配送问题,并以最小化配送时间为目标建立混合整数规划模型(MIP).为解决该问题,设计K-means聚类和最近邻协同的初始解生成算法,并提出基...针对物流配送需求大、“最后一公里”交付困难等问题,提出带有动态能耗约束的多车辆与多无人机协同配送问题,并以最小化配送时间为目标建立混合整数规划模型(MIP).为解决该问题,设计K-means聚类和最近邻协同的初始解生成算法,并提出基于问题领域知识的自适应大规模邻域搜索算法(adaptive large neighborhood search,ALNS).在不同规模算例上的实验结果表明,所提出的算法相比于模拟退火算法、变邻域搜索算法和遗传算法在求解质量和求解效率方面都具有一定的优势,求解质量分别平均提升23.8%、23.3%和5.7%,表明ALNS较对比算法能够更好地平衡全局搜索和局部搜索.此外.灵敏度分析实验表明,无人机载重能力和无人机续航能力是影响包裹配送时间的两个关键因素.展开更多
Video processing is one challenge in collecting vehicle trajectories from unmanned aerial vehicle(UAV) and road boundary estimation is one way to improve the video processing algorithms. However, current methods do no...Video processing is one challenge in collecting vehicle trajectories from unmanned aerial vehicle(UAV) and road boundary estimation is one way to improve the video processing algorithms. However, current methods do not work well for low volume road, which is not well-marked and with noises such as vehicle tracks. A fusion-based method termed Dempster-Shafer-based road detection(DSRD) is proposed to address this issue. This method detects road boundary by combining multiple information sources using Dempster-Shafer theory(DST). In order to test the performance of the proposed method, two field experiments were conducted, one of which was on a highway partially covered by snow and another was on a dense traffic highway. The results show that DSRD is robust and accurate, whose detection rates are 100% and 99.8% compared with manual detection results. Then, DSRD is adopted to improve UAV video processing algorithm, and the vehicle detection and tracking rate are improved by 2.7% and 5.5%,respectively. Also, the computation time has decreased by 5% and 8.3% for two experiments, respectively.展开更多
This paper proposed an improved artificial physics(AP)method to solve the autonomous navigation problem for multiple unmanned aerial vehicles(UAVs)/unmanned ground vehicles(UGVs)heterogeneous coordination in the three...This paper proposed an improved artificial physics(AP)method to solve the autonomous navigation problem for multiple unmanned aerial vehicles(UAVs)/unmanned ground vehicles(UGVs)heterogeneous coordination in the three-dimensional space.The basic AP method has a shortcoming of easily plunging into a local optimal solution,which can result in navigation fails.To avoid the local optimum,we improved the AP method with a random scheme.In the improved AP method,random forces are used to make heterogeneous multi-UAVs/UGVs escape from local optimum and achieve global optimum.Experimental results showed that the improved AP method can achieve smoother trajectories and smaller time consumption than the basic AP method and basic potential field method(PFM).展开更多
文摘车辆与无人机联合配送模式在产业界受到青睐,该模式有效地降低了配送成本,但却有极大的调度难度,问题的求解也非常复杂。本文对问题进行明确定义并建立模型,根据问题特性设计了一个自适应大规模邻域搜索(Adaptive Large Neighborhood Search,ALNS)算法,进行了大量的实验的对比和分析。研究结果表明,ALNS算法相比Gurobi在运行时间上有明显优势,结果相同甚至更优;车辆与无人机联合配送模式也较仅卡车配送模式节约了成本。
文摘针对物流配送需求大、“最后一公里”交付困难等问题,提出带有动态能耗约束的多车辆与多无人机协同配送问题,并以最小化配送时间为目标建立混合整数规划模型(MIP).为解决该问题,设计K-means聚类和最近邻协同的初始解生成算法,并提出基于问题领域知识的自适应大规模邻域搜索算法(adaptive large neighborhood search,ALNS).在不同规模算例上的实验结果表明,所提出的算法相比于模拟退火算法、变邻域搜索算法和遗传算法在求解质量和求解效率方面都具有一定的优势,求解质量分别平均提升23.8%、23.3%和5.7%,表明ALNS较对比算法能够更好地平衡全局搜索和局部搜索.此外.灵敏度分析实验表明,无人机载重能力和无人机续航能力是影响包裹配送时间的两个关键因素.
基金Project(2009AA11Z220)supported by the National High Technology Research and Development Program of China
文摘Video processing is one challenge in collecting vehicle trajectories from unmanned aerial vehicle(UAV) and road boundary estimation is one way to improve the video processing algorithms. However, current methods do not work well for low volume road, which is not well-marked and with noises such as vehicle tracks. A fusion-based method termed Dempster-Shafer-based road detection(DSRD) is proposed to address this issue. This method detects road boundary by combining multiple information sources using Dempster-Shafer theory(DST). In order to test the performance of the proposed method, two field experiments were conducted, one of which was on a highway partially covered by snow and another was on a dense traffic highway. The results show that DSRD is robust and accurate, whose detection rates are 100% and 99.8% compared with manual detection results. Then, DSRD is adopted to improve UAV video processing algorithm, and the vehicle detection and tracking rate are improved by 2.7% and 5.5%,respectively. Also, the computation time has decreased by 5% and 8.3% for two experiments, respectively.
基金supported by the National Natural Science Foundation of China(Grant Nos.61273054,60975072)the National Basic Research Program of China("973" Project)(Grant No.2013CB035503)+3 种基金the Program for New Century Excellent Talents in University of China(Grant No.NCET-10-0021)the Top-Notch Young Talents Program of Chinathe Fundamental Research Funds for the Central Universities of Chinathe Aeronautical Foundation of China(Grant No.20115151019)
文摘This paper proposed an improved artificial physics(AP)method to solve the autonomous navigation problem for multiple unmanned aerial vehicles(UAVs)/unmanned ground vehicles(UGVs)heterogeneous coordination in the three-dimensional space.The basic AP method has a shortcoming of easily plunging into a local optimal solution,which can result in navigation fails.To avoid the local optimum,we improved the AP method with a random scheme.In the improved AP method,random forces are used to make heterogeneous multi-UAVs/UGVs escape from local optimum and achieve global optimum.Experimental results showed that the improved AP method can achieve smoother trajectories and smaller time consumption than the basic AP method and basic potential field method(PFM).
