Research on UAV path planning method based on improved HPO algorithm in multi-task environment

When UAVs are carrying out missions, they are faced with complex task environments. To enhance the adaptability of UAV applications, it is required that they possess fast path-planning capabilities. This paper takes the execution of complex tasks by multiple UAVs in a three-dimensional environment as background and transforms the path-planning problem into a multi-constraint optimization problem. Innovatively combining the HPO algorithm and task allocation mechanism, this study achieves collaborative path planning for multiple UAVs for complex tasks. In order to improve the optimization speed of the HPO algorithm, the following improvements have been made: firstly, introducing the chaotic mapping model to improve the performance of population initialization; secondly, adopting the golden sine strategy to change the population update strategy and accelerate the convergence speed of the algorithm. Then, the single-peak and multi-peak benchmark functions are used to test the average, standard deviation, and optimal values of the algorithm. Finally, path planning experiments are carried out in a three-dimensional map with multiple task points and obstacles, and the results show that the AGSHPO algorithm improves the robustness and real-time performance of the UAVs when executing complex tasks to a certain extent.