Adaptive Budgerigar Optimization-Based Obstacle Avoidance Path Planning for Unmanned Aerial Vehicle

This paper addresses the path planning problem of Unmanned Aerial Vehicle (UAV) operating in a three-dimensional environment populated with obstacles. An enhanced Adaptive Budgerigar Optimization (ABO) algorithm is designed to navigate the UAV efficiently, ensuring collision avoidance while maintaining high solution accuracy. The primary innovation of our approach involves modifying the iteration update formula of the original Parrot Optimization algorithm by incorporating an adaptive adjustment factor. This factor dynamically regulates the convergence rate and accuracy, thereby enabling the algorithm to escape local optima and achieve globally optimal paths effectively. Through comprehensive simulation experiments, we compare the performance of the ABO algorithm against traditional Particle Swarm Optimization (PSO) and the original Budgerigar Optimization algorithm. The results demonstrate the superior convergence speed and solution quality of the proposed algorithm, thereby validating its effectiveness and feasibility.