UAV dynamic obstacle avoidance path planning based on improved crested porcupine optimization algorithm

To enhance the ability of unmanned aerial vehicle (UAV) to avoid dynamic obstacles and improve convergence speed and accuracy in complex environments, a path planning method for UAV based on the improved Crested Porcupine Optimization (ICPO) algorithm is proposed. By adjusting the search strategy to avoid premature convergence to local optima and enhance global search capabilities, this method aims to improve the performance of UAV in dynamic obstacle avoidance. Firstly, a terrain model is established and the objective function is constructed. Secondly, a Random-Elite Differential Mutation strategy is introduced in the third defense stage to prevent premature convergence to local optima. Then, a Dynamic Opposite Learning strategy is introduced in the fourth defense stage to optimize solutions through reverse solution updates, ensuring rapid approximation to the global optimum in the early stage and enhanced local search attention in the later stage. Finally, experiments and simulations are conducted using MATLAB to evaluate the ICPO algorithm. The results show that the ICPO algorithm outperforms the Crested Porcupine Optimization (CPO), Spider Wasp Optimization (SWO), Dragonfly Algorithm (DA), and Tunicate Swarm Algorithm (TSA) in terms of obtaining a short and safe path while accurately avoiding obstacles