一种巡检无人机航迹自适应规划算法

A Path Adaptive Planning Algorithm for Patrol Unmanned Aerial Vehicle

关键词:  巡检无人机  航迹规划  高斯云  α分布  白鲸优化器

Key words:  patrol UAV  path planning  Gaussian cloud  α distribution  Beluga whale optimizer

摘要:

针对传统方法求解复杂环境中巡检无人机航迹规划容易出现精度差、收敛于局部最优航迹的不足，提出一种自适应高斯云与α分布变异白鲸优化器的巡检无人机航迹规划算法。引入PWLCM混沌映射-准对立学习初始化策略提升初始航迹解的多样性和质量，利用自适应高斯云策略提升算法对全局最优航迹的搜索能力，设计自适应α分布变异策略使算法跳离局部最优航迹，丰富搜索空间并提高收敛速度。构建了巡检无人机航迹规划模型，设计了考虑航程大小、高度、转角及障碍物威胁的权重代价函数，将三维航迹规划问题转换为约束条件多目标优化问题，并通过自适应高斯云与α分布变异白鲸优化器迭代搜索巡检无人机最优航迹。结果表明，改进算法搜索航迹可安全避障，且代价更小，有助于提高巡检任务效率和安全性。

Abstract:

In order to solve the problem of path planning of UAV in complex environment, the traditional methods tend to have poor accuracy and converge to a local optimal track.An adaptive Gaussian cloud and α distribution variation Beluga optimization algorithm for tracking UAV is proposed. The PWLCM chaotic map-quasi-oppositional learning initialization is introduced to improve the diversity and quality of the initial path solutions. An adaptive Gaussian cloud strategy is used to improve the search ability of the algorithm for the global optimal path. An adaptive α distribution variation is designed to make the algorithm skip a local optimal path, enrich the search space and improve the convergence speed.The path planning model of patrol UAV is constructed,and the weight cost function considering the range size,height,angle and obstacle threat is designed.The three-dimensional trajectory planning problem is transformed into a multi-objective optimization problem with constraints.The optimal path of patrol UAV is iteratively searched by adaptive Gaussian cloud and α distribution variation Beluga optimizer.The results show that the improved algorithm can safely avoid obstacles and the cost is less,which is helpful to improve the efficiency and security of patrol tasks.