基于自适应郊狼算法的无人机离线航迹规划

UAV offline path planning based on self-adaptive coyote optimization algorithm

针对无人机(unmanned aerial vehicle,UAV)离线航迹规划对算法全局搜索能力和鲁棒性的要求,设计一种自适应郊狼算法,从最优化问题角度研究UAV离线航迹规划。建立UAV离线航迹规划的数学模型;在标准郊狼优化算法的基础上设计4种操作算子和一种自适应学习机制,使算法在搜索的过程中,智能选择合适的操作算子,并设计莱维飞行策略提高算法的全局寻优能力;最后对自适应郊狼算法进行函数测试和离线航迹规划仿真。函数测试表明自适应郊狼算法具有较强的全局搜索能力,离线航迹规划仿真表明自适应郊狼优化算法能适应不同维数的离线航迹规划问题。

To satisfy the requirements of unmanned aerial vehicle(UAV)offline path planning for the algorithm’s global search capability and robustness,a self-adaptive coyote optimization algorithm is designed to study UAV offline path planning from the perspective of optimization problems.A mathematical model is established for UAV offline path planning.On the basis of the coyote optimization algorithm,four operators and an adaptive learning mechanism are designed to enable the algorithm to intelligently select the appropriate operator during the search process,and design the Levy flight strategy to improve the algorithm’s global search ability.Finally,the function test and offline path planning simulation are carried out for the self-adaptive coyote optimization algorithm.The function test shows that the self-adaptive coyote optimization algorithm has a strong global search ability,and the offline path planning simulation shows that the self-adaptive coyote optimization algorithm can adapt to the offline path planning problem of different dimensions.