基于鸽群优化的复杂环境下无人机侦查航迹优化
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摘要
地表环境的复杂性以及机载探测装置探测范围的约束,使得无人机在某些方位无法实现对地面目标的有效监测.因此,在设计无人机侦查航路时需要综合考虑无人机飞行性能与目标可视范围等条件.针对这一问题,提出了一种基于改进鸽群优化与动态规划算法的无人机侦查航路优化方法.首先,通过分析机载探测装置视场与地表环境的相对空间关系,得到了目标周围空域的可视范围.随后,结合鸽群优化理论框架与动态规范方法对无人机的最优侦查航迹进行求解.为提高鸽群优化算法在求解目标排序问题中的效率,提出了一种离散化的鸽群优化改进机制.仿真结果表明,侦查航迹优化算法在提高任务完成度的同时具有很高的求解效率和准确性.
In this paper,a trajectory planning approach based on the principle of dynamic programming and framework of pigeon inspired optimization( PIO) was proposed for UAV surveillance tasks. In this approach,the sensor visibility was firstly analyzed by considering the occlusions caused by terrain feature,and the delectable areas of the targets were approximated by a series of polygons. To determine the optimal trackable path to cover all target sites,the target visibility polygons were replaced by with their centers firstly,which allowed to obtained an initial solution by optimizing the order of the targets to be visited. In the following step of the algorithm,a path refinement scheme combing dynamic programming and PIO was proposed to refine the initial route by considering the sensor visibility and turning radius constraint of the UAV. Comparative simulation proved the performance of the proposed algorithm in terms of efficiency and accuracy.
In this paper,a trajectory planning approach based on the principle of dynamic programming and framework of pigeon inspired optimization( PIO) was proposed for UAV surveillance tasks. In this approach,the sensor visibility was firstly analyzed by considering the occlusions caused by terrain feature,and the delectable areas of the targets were approximated by a series of polygons. To determine the optimal trackable path to cover all target sites,the target visibility polygons were replaced by with their centers firstly,which allowed to obtained an initial solution by optimizing the order of the targets to be visited. In the following step of the algorithm,a path refinement scheme combing dynamic programming and PIO was proposed to refine the initial route by considering the sensor visibility and turning radius constraint of the UAV. Comparative simulation proved the performance of the proposed algorithm in terms of efficiency and accuracy.
引文
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[2]金婷,方欢,方贤文.改进型Dijkstra算法的最短路径求解[J].软件导刊,2016,15(2):129-131.
[3]丁家如,杜昌平,赵耀,等.基于改进人工势场法的无人机路径规划算法[J].计算机应用,2016,36(1):287-290.
[4]FU Y G,DING M Y,ZHOU C P.Phase angleencoded and quantum-behaved particle swarm optimization applied to three-dimensional route planning for UAV[J].IEEE transactions on systems,man and cybernetics-Part A:Systems and humans,2012,42(2):511-526.
[5]BELKHOUCHE F.Reactive path planning in a dynamic environment[J].IEEE transactions on robotics,2009,25(4):902-911.
[6]尹高扬,周绍磊,吴青坡.基于改进RRT算法的无人机航迹规划[J].电子学报,2017,45(7):1764-1769.
[7]穆瑞杰.基于遗传算法的地铁车站引导标识布点探析[J].郑州大学学报(工学版),2018,39(1):73-89.
[8]李文,伍铁斌,赵全友,等.改进的混沌粒子群算法在TSP中的应用[J].计算机应用研究,2015,32(7):2065-2067.
[9]梁静,宋慧,瞿博阳,等.基于改进粒子群算法的路径优化问题研究[J].郑州大学学报(工学版),2014,35(1):34-38.
[10]WANG Y,CAO Y.Coordinated target tracking via a hybrid optimization approach[J].Sensors,2017,17(3):472.
[11]邱华鑫,段海滨,范彦铭.基于鸽群行为机制的多无人机自主编队[J].控制理论与应用,2015,32(10):1298-1304.