Path Planning of an Unmanned Aerial Vehicle Based on a Multi-Strategy Improved Pelican Optimization Algorithm.

IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY
Shaoming Qiu, Jikun Dai, Dongsheng Zhao
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Abstract

The UAV path planning algorithm has many applications in urban environments, where an effective algorithm can enhance the efficiency of UAV tasks. The main concept of UAV path planning is to find the optimal flight path while avoiding collisions. This paper transforms the path planning problem into a multi-constraint optimization problem by considering three costs: path length, turning angle, and collision avoidance. A multi-strategy improved POA algorithm (IPOA) is proposed to address this. Specifically, by incorporating the iterative chaotic mapping method with refracted reverse learning strategy, nonlinear inertia weight factors, the Levy flight mechanism, and adaptive t-distribution variation, the convergence accuracy and speed of the POA algorithm are enhanced. In the CEC2022 test functions, IPOA outperformed other algorithms in 69.4% of cases. In the real map simulation experiment, compared to POA, the path length, turning angle, distance to obstacles, and flight time improved by 8.44%, 5.82%, 4.07%, and 9.36%, respectively. Similarly, compared to MPOA, the improvements were 4.09%, 0.76%, 1.85%, and 4.21%, respectively.

基于多策略改进鹈鹕优化算法的无人飞行器路径规划
无人机路径规划算法在城市环境中有许多应用,有效的算法可以提高无人机执行任务的效率。无人机路径规划的主要概念是在避免碰撞的同时找到最优飞行路径。本文通过考虑路径长度、转弯角度和避免碰撞三个代价,将路径规划问题转化为多约束优化问题。为此,本文提出了一种多策略改进 POA 算法(IPOA)。具体来说,通过结合具有折射反向学习策略的迭代混沌映射法、非线性惯性权重因子、利维飞行机制和自适应 t 分布变化,提高了 POA 算法的收敛精度和速度。在 CEC2022 测试函数中,IPOA 在 69.4% 的情况下优于其他算法。在真实地图仿真实验中,与 POA 相比,路径长度、转弯角度、障碍物距离和飞行时间分别提高了 8.44%、5.82%、4.07% 和 9.36%。同样,与 MPOA 相比,分别提高了 4.09%、0.76%、1.85% 和 4.21%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
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