Qiu Shaoming, Dai Jikun, Zhao Dongsheng
Key Laboratory of Network and Communications, Dalian University, Dalian 116622, China.
School of Economics and Management, Ningxia University, Yinchuan 750021, China.
Biomimetics (Basel). 2024 Oct 21;9(10):647. doi: 10.3390/biomimetics9100647.
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.
无人机路径规划算法在城市环境中有许多应用，其中有效的算法可以提高无人机任务的效率。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、转弯角度和避碰三种成本，将路径规划问题转化为多约束优化问题。为此提出了一种多策略改进的粒子群优化算法（IPOA）。具体而言，通过将迭代混沌映射方法与折射反向学习策略、非线性惯性权重因子、莱维飞行机制和自适应t分布变异相结合，提高了粒子群优化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。在真实地图模拟实验中，与粒子群优化算法相比，路径长度、转弯角度、到障碍物的距离和飞行时间分别提高了8.44%、5.82%、4.07%和9.36%。同样，与多策略粒子群优化算法相比，改进分别为4.09%、0.76%、1.85%和4.21%。
