三维无人机路径规划问题旨在满足安全性条件的前提下为无人机规划出一条最佳的飞行路径.本文通过数学建模的方式构建出无人机路径规划的成本函数,从而无人机路径规划问题转化为多约束的优化问题,并使用元启发式算法来求解该问题.针对人工兔优化算法收敛慢以及易陷入局部最优的缺陷,本文开发了一种基于Levy飞行、自适应柯西变异以及精英群遗传策略改进的人工兔优化算法(Artificial Rabbit Optimization algorithm based on Levy flight,adaptive Cauchy mutation,and elite population Genetic strategy,LCGARO).将LCGARO与6个经典和先进的元启发式算法在29个CEC2017测试函数和6个复杂度不同的三维无人机路径规划地形场景中进行多方面对比实验.对比实验结果证明,在CEC2017测试函数的对比实验中,本文提出的LCGARO算法在22个测试函数中具有更优的寻优精度.在无人机路径规划实验中,LCGARO算法在5个地形场景中能够规划出总成本函数值最小的飞行路径.
A 3D UAV Path Planning Method Based on Multi-Strategy Improved Artificial Rabbit Optimization Algorithm
The 3D UAV(Unmanned Aerial Vehicle)path planning problem aims to plan an optimal flight path for the UAV while satisfying safety conditions.In this paper,a cost function for UAV path planning is constructed by means of mathematical modeling,so that the UAV path planning problem is transformed into a multi-constrained optimization prob-lem,and metaheuristic algorithms are applied to solve this problem.Aiming at the shortcomings of artificial rabbit optimiza-tion algorithm which is slow to converge and easy to fall into local optimum,this paper develops an improved Artificial Rabbit Optimization algorithm based on Levy flight,adaptive Cauchy mutation,and elite population Genetic strategy(LC-GARO).Multifaceted comparison experiments are conducted between LCGARO and six classical and advanced heuristic al-gorithms in 29 CEC2017 test functions and six 3D UAV path-planning terrain scenarios of varying complexity.The results of the comparison experiments prove that the LCGARO algorithm proposed in this paper has better optimization accuracy among 22 test functions in the comparison experiments of CEC2017 test functions.In the UAV path planning experiments,the LCGARO algorithm is able to plan a flight path with the smallest total cost function value in five terrain scenarios.
3D UAV(Unmanned Aerial Vehicle)path planningartificial rabbit optimization algorithmLevy flightadaptive Cauchy mutationelite group genetic strategymetaheuristic algorithm
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维普
