基于 TSACO及动态避障策略的无人机路径规划.

This paper proposed an improved ant colony optimization named TSACO and an enhanced velocity obstacle (VO) to solve the UAV path planning problem. The TSACO incorporated non-uniform initial pheromone distribution, a turning heuristic function, and an elite ant system to improve the convergence speed and reduce the number of path corners during global planning. The enhanced VO integrated the UAV's dynamic equation, adaptive collision radius and emergency collision cone, along with an optimal velocity selection approach to increase real-time and safe local obstacle avoidance during local planning. Simulation experiments demonstrate that the proposed algorithms have better effectiveness in terms of path length, number of turns, and dynamic obstacle avoidance compared to other algorithms. [ABSTRACT FROM AUTHOR]