基于改进蜣螂优化算法的无人机三维路径规划

Unmanned aerial vehicle three-dimensional path planning based on improved dung beetle optimization algorithm

无人机(UAV)三维路径规划问题是十分复杂的全局优化问题,但基于启发式优化算法的无人机路径规划存在速度慢,精度不足的问题。针对此问题,提出一种改进蜣螂优化算法的UAV路径规划方法。首先,提出一种通过引入Bernoulli混沌映射、可变螺旋搜索策略、新型惯性权重和Levy飞行策略改进的蜣螂优化算法(BCLDBO)。通过与其他算法在6个基准测试函数上进行实验对比,证明BCLDBO算法寻优精度更高,收敛速度更快。其次,通过航迹长度成本、高度成本、平滑成本和威胁成本建立路径规划目标函数,并构建复杂度不同的三维任务空间。最后,将BCLDBO算法应用于UAV三维路径规划问题中,证明此算法较其他算法的路径成本更低,路径规划效果更好。

The three-dimensional path planning problem of unmanned aerial vehicle(UAV)is a very complex global optimization problem.However,UAV path planning based on heuristic optimization algorithms has the problems of slow speed and insufficient accuracy.To solve this problem,a UAV path planning method that improves the dung beetle optimization algorithm is proposed.First,an improved dung beetle optimization algorithm(BCLDBO)is proposed by introducing Bernoulli chaos map,variable spiral search strategy,new inertia weight and Levy flight strategy.Through experimental comparison with other algorithms on six benchmark test functions,it is proved that the BCLDBO algorithm has higher optimization accuracy and faster convergence speed.Secondly,the path planning objective function is established through the track length cost,height cost,smoothing cost and threat cost,and three-dimensional mission spaces with different complexities are constructed.Finally,the BCLDBO algorithm is applied to the UAV three-dimensional path planning problem,which proves that this algorithm has lower path cost and better path planning effect than other algorithms.