Zhang Ran, Li Sen, Ding Yuanming, Qin Xutong, Xia Qingyu
School of Information Engineering, Dalian University, Dalian 116622, China.
Communication and Network Laboratory, Dalian University, Dalian 116622, China.
Sensors (Basel). 2022 Jul 13;22(14):5232. doi: 10.3390/s22145232.
In the Unmanned Aerial Vehicle (UAV) system, finding a flight planning path with low cost and fast search speed is an important problem. However, in the complex three-dimensional (3D) flight environment, the planning effect of many algorithms is not ideal. In order to improve its performance, this paper proposes a UAV path planning algorithm based on improved Harris Hawks Optimization (HHO). A 3D mission space model and a flight path cost function are first established to transform the path planning problem into a multidimensional function optimization problem. HHO is then improved for path planning, where the Cauchy mutation strategy and adaptive weight are introduced in the exploration process in order to increase the population diversity, expand the search space and improve the search ability. In addition, in order to reduce the possibility of falling into local extremum, the Sine-cosine Algorithm (SCA) is used and its oscillation characteristics are considered to gradually converge to the optimal solution. The simulation results show that the proposed algorithm has high optimization accuracy, convergence speed and robustness, and it can generate a more optimized path planning result for UAVs.
在无人机（UAV）系统中，寻找低成本且搜索速度快的飞行规划路径是一个重要问题。然而，在复杂的三维（3D）飞行环境中，许多算法的规划效果并不理想。为了提高其性能，本文提出了一种基于改进哈里斯鹰优化（HHO）的无人机路径规划算法。首先建立三维任务空间模型和飞行路径代价函数，将路径规划问题转化为多维函数优化问题。然后对HHO进行路径规划改进，在探索过程中引入柯西变异策略和自适应权重，以增加种群多样性、扩大搜索空间并提高搜索能力。此外，为了降低陷入局部极值的可能性，使用正弦余弦算法（SCA）并考虑其振荡特性逐步收敛到最优解。仿真结果表明，所提算法具有较高的优化精度、收敛速度和鲁棒性，能够为无人机生成更优的路径规划结果。
