基于双混沌改进的萤火虫算法无人机路径规划

AUV path planning based on improved firefly algorithm based on double chaos

摘要:

针对无人机在自适应巡航路径规划存在的效率低、规划困难等问题,提出一种多角度改进的萤火虫算法。首先利用Chebyshev混沌特性初始化种群,改善了初始种群不易产生的问题；针对步长因子过于固定的问题,引入Levy飞行策略改进位置更新公式和步长更新公式,提高了种群的搜索范围和有效性；其次利用logistic混沌变异改进吸引度系数,提高了个体跳出当前状态逃离局部陷阱解的概率,加快收敛速度；最后基于建立的优化函数进行仿真,结果表明,改进后路径长度减少7.47%,节点减少31.57%,平顺度优于改进前,收敛时间减少18.54%,取得很好的收敛效果,有助于无人机在真实场景完成飞行作业。

Abstract:

Aiming at the problems of low efficiency and difficulty in adaptive cruise path planning for unmanned aerial vehicle(UAV), a multi-angle improved firefly algorithm was proposed. Firstly, Chebyshev chaotic characteristics were used to initialize the population, which improved the problem that the initial population was not easy to produce. To solve the problem that the step factor is too fixed, Levy flight strategy is introduced to improve the position update formula and step update formula, so as to improve the search range and effectiveness of the population. Then, logistic chaos variation was used to improve the attraction coefficient, which increased the probability of individuals jumping out of the current state and escaping from the local optimal solution, and accelerated the rate of convergence. Finally, based on the established optimization function, the simulation results show that the improved path length is reduced by 7.47%, the node is reduced by 15.79%, the smoothness is better than before, the convergence time is reduced by 18.54%, and the convergence effect is good, it helps UAV to complete flight operation in real scene.