融合多策略改进黑猩猩优化算法的UAV航迹规划

Path Planning for UAVs Based on Multi-strategy Improved Chimp Optimization Algorithm

针对三维复杂环境中无人机航迹规划容易出现搜索停滞、收敛于局部最优的不足,提出一种多策略混合改进黑猩猩优化算法的航迹规划方法。针对黑猩猩优化算法寻优精度不足的问题,引入收敛因子非线性更新均衡算法全局搜索与局部开发能力;设计权重因子避免个体跟随的盲目性及迭代后期个体趋于同化,提升搜索精度;设计黄金正弦莱维飞行引导机制防止因多样性逐步贫化而陷入局部最优。利用改进黑猩猩算法求解无人机航迹规划,结合无人机飞行环境三维地形图构建航迹规划模型,设计多约束飞行代价函数,并将其作为适应度函数,对无人机三维航迹规划方案迭代求解。结果表明,改进算法能够搜索到一条安全避障且航迹代价更小的路径,搜索精度高于类比算法。

In view of the shortcomings of UAV path planning in 3D complexity environment such as search stagnation and convergence to local optimization,an improved chimp optimization algorithm based on multiple strategies is proposed.To address the shortcomings of the optimization accuracy of the chimp algorithm,a convergence factor nonlinear update strategy is introduced to balance the global search and local development capabilities.A weight factor is designed to avoid the blindness of individual following and the assimilation of individuals in the later stage of iteration for improving the search accuracy.A golden sine Levy flight guidance mechanism is designed to prevent falling into local optimization due to the gradual dilution of diversity.The improved chimp optimization algorithm is used to solve the UAV path planning problem for constructing a path planning model by use of 3D terrain map of the UAV flight environment,designing a multi-constraint flight cost function and using it as a fitness function,and iteratively solving the UAV 3D path planning scheme.The results show that the improved algorithm can search for a safe obstacle avoidance path with lower trajectory cost,and the search accuracy is higher than that of analog algorithms.