@inproceedings{8a80437f9b91432291634177f51aac21,
title = "UAV 3D Environmental Track Planning Based on Improved Ant Colony Algorithm",
abstract = "This paper proposes an improved algorithm for the UAV trajectory planning problem. The algorithm improves the performance of Ant Colony Algorithm (ACO) by solving the existing problems. First, the improved algorithm adjusts the initial pheromone size according to the distance between the front and rear nodes to avoid the randomness of the ants at the beginning of the algorithm. Then, a heuristic function is added to improve the convergence speed of the algorithm. Simultaneously, in order to reduce the influence of the worst path on the subsequent iterative process, the pheromone update rule is changed in the improved algorithm. Finally, through the comparison of the simulation experiments of the two algorithms in the same environment, the simulation results show that the improved algorithm has faster convergence speed, and the optimal fitness value and algorithm time-consuming are improved.",
keywords = "ACO, Pheromones, Trajectory planning, UAV",
author = "Kang Yang and Hao Xiong and Hongbin Deng",
note = "Publisher Copyright: {\textcopyright} 2023, Beijing HIWING Sci. and Tech. Info Inst.; International Conference on Autonomous Unmanned Systems, ICAUS 2022 ; Conference date: 23-09-2022 Through 25-09-2022",
year = "2023",
doi = "10.1007/978-981-99-0479-2_177",
language = "English",
isbn = "9789819904785",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "1908--1915",
editor = "Wenxing Fu and Mancang Gu and Yifeng Niu",
booktitle = "Proceedings of 2022 International Conference on Autonomous Unmanned Systems, ICAUS 2022",
address = "Germany",
}