基于改进蚁群算法的山区无人机路径规划方法
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摘要
对无人机在山区执行应急物资运输任务时的飞行路径规划问题进行研究.基于对无人机的性能分析与比选,探讨了路径规划的约束条件,提出了一种考虑路径安全度的改进蚁群算法.首先,基于高海拔山峰的位置构造泰森多边形,获取无人机在山区避障飞行条件下的路径可行解;其次,为避开山峰密集区域,建立路径安全度约束,缩小可行解范围;进而,利用蚁群算法搜索最短路径;最后,消除路径中不必要的障碍点以进一步缩短距离,并综合考虑无人机性能参数对拐角进行平滑处理,获得最终可用于实际飞行的最优安全路径.算例分析表明,改进的蚁群算法较传统算法收敛速度更快,且生成的路径更短.
This paper studies the UAV flight route planning problem when carrying out emergency cargo transportation mission in mountainous area. Based on the performance analysis and comparison of UAVs,constraints of path planning is discussed, and an improved ant colony algorithm considering path safety is proposed. Firstly, a Tyson polygon is formulated based on the location of high altitude mountains, and then a feasible UAV path solution under flight condition of obstacle avoidance in mountainous area is obtained. Secondly,in order to avoid dense peaks area, path safety constraints are built. Thirdly, the shortest path is searched by ant colony algorithm. Finally, the unnecessary obstacles in the path are eliminated so as to further shorten the distance,and the corners are smoothed considering the performance parameters of UAVs, generating the optimal safe path which can ultimately be used for an actual flight. The example analysis shows the improved ant colony algorithm converges faster and generates shorter path than traditional one.
This paper studies the UAV flight route planning problem when carrying out emergency cargo transportation mission in mountainous area. Based on the performance analysis and comparison of UAVs,constraints of path planning is discussed, and an improved ant colony algorithm considering path safety is proposed. Firstly, a Tyson polygon is formulated based on the location of high altitude mountains, and then a feasible UAV path solution under flight condition of obstacle avoidance in mountainous area is obtained. Secondly,in order to avoid dense peaks area, path safety constraints are built. Thirdly, the shortest path is searched by ant colony algorithm. Finally, the unnecessary obstacles in the path are eliminated so as to further shorten the distance,and the corners are smoothed considering the performance parameters of UAVs, generating the optimal safe path which can ultimately be used for an actual flight. The example analysis shows the improved ant colony algorithm converges faster and generates shorter path than traditional one.
引文
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[2]JENNINGS A L,ORDONEZ R,CECCARELLI N.Dynamic programming applied to UAV way point path planning in wind[C].IEEE International Conference on Computer-aided Control Systems,2008.
[3]ZHANG Y,WAN X Y,ZHENG X D,et al.Cellular genetic algorithm for multi objective optimization based on orthogonal design[J].Acta Electronica Sinica,2016,23(10):4742-4746.
[4]GAUTAM S A,VERMA N.Path planning for unmanned aerial vehicle based on genetic algorithm and artificial neural network in 2D[J].International Journal for Scientific Research&Development,2014,2(2):2321-0613.
[5]ROBERGE V,TARBOUCHI M,LABONTE G.Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning[J].IEEE Transactions on Industrial Informatics,2013,9(1):132-141.
[6]李喜刚,蔡远利.基于改进蚁群算法的无人机路径规划[J].飞行力学,2017,35(1):52-56.[LI X G,CAI Y L.UAV path planning based on improved ant colony algorithm[J].Flight Dynamics,2017,35(1):52-56.]
[7]刘少华,罗小龙,何幼斌,等.基于Delauany三角网的泰森多边形生成算法研究[J].长江大学学报(自科版),2007,4(1):100-103.[LIU S H,LUO X L,HE Y B,et al.Research on tyson polygon generation algorithm based on delauany triangulation[J].Journal of Yangtze University(Nat Sci Edit),2007,4(1):100-103.]