Obstacle Avoidance Path Planning Using the Elite Ant Colony Algorithm for Parameter Optimization of Unmanned Aerial Vehicles - Meng, Xiaoling; Zhu, Xijing; Zhao, Jing (Arabian journal for science and engineering (2011), 2023)
Unmanned aerial vehicles (UAVs) have attracted considerable research attention because of their strong interoperability, high flexibility, and excellent maneuverability. Path planning and autonomous obstacle avoidance are critical for UAVs. In this study, multiobjective optimization using the ant colony algorithm was performed for solving the UAV obstacle avoidance path planning problem. To overcome the easy-to-fall-into-deadlock tendency and slow convergence speed of the conventional ant colony algorithm, an elite ant colony algorithm was proposed for improving path selection probability and pheromone update strategy. Next, the response surface method was used to analyze the key parameters in the improved algorithm, construct the regression prediction model of response indicators, perform variance analysis, and verify the reliability of the model. The key parameters were optimized to obtain the best parameter combination, and simulation experiments were conducted. The results revealed that the performance of path length, running time, and robustness in various terrains improved considerably. Thus the proposed method is a feasible scheme for the path planning of UAVs in military search and rescue and material transportation.