UAV Path Planning Algorithm Based on Improved Harris Hawks Optimization

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.

Automated summary

This paper proposes a UAV path planning algorithm based on improved Harris Hawks Optimization (HHO). By establishing a 3D mission space model and a flight path cost function, the path planning problem is transformed into a multidimensional function optimization problem. In the improved HHO algorithm, the Cauchy mutation strategy and adaptive weight are introduced to increase population diversity, expand the search space, and improve search ability. In addition, the Sine-cosine Algorithm (SCA) is used to gradually converge to the optimal solution and reduce the possibility of falling into local extremum. Simulation results show that the proposed algorithm has high optimization accuracy, convergence speed, and robustness, and can generate more optimized path planning results for UAVs.