UAV route planning based on improved whale optimization algorithm and dynamic artificial potential field method

Abstract

Aiming at the problem that the global path optimization cannot be guaranteed in the dynamic path planning of rotorcraft formation, a static path planning and dynamic obstacle avoidance algorithm combining the improved whale optimization algorithm and dynamic artificial potential field method is proposed. The optimization results of whale optimization algorithm are greatly affected by the distribution of initial solutions. The paper proposes to combine the hierarchical system of grey wolf optimization algorithm with the standard whale algorithm, and incorporate the first three historical optimal solutions into the calculation range of potential optimal solutions to improve the ability of the population to escape from the value of local minimum. The commonly used artificial potential field method includes the problem of target reachability and local minima. This paper improves the classical exclusion function model. On the basis of the improved model, the dynamic potential field model is added to realize the avoidance of dynamic obstacles. The simulation results show that the path planning ability of rotorcraft UAVs has been improved through the improvement of whale optimization algorithm and artificial potential field model, and the formation UAVs have the static path planning and dynamic obstacle avoidance ability. At the same time, it has great advantages in convergence speed and solution accuracy.