The Flight Navigation Planning Based on Potential Field Ant Colony Algorithm
- 10.2991/acaai-18.2018.47How to use a DOI?
- path planning; Ant Colony Optimal; Artificial Potential Field; UAV
Path planning in complex environment is the main foundation of flights automation navigation. Therefore, this paper aims to propose an algorithm combined ant colony optimal algorithm with potential field heuristic information. The main implement method of this algorithm is to use the information between the environment and goal to build the heuristic elements. What's more, the method defines this information as initial pheromone and converses elements to guide the ants to find the shortest path. Thanks to the heuristic elements in algorithm,the PFACO algorithm has a better searching tendency than traditional ACO algorithm,and overcome disadvantage in ants blindness. Through the simulation, the results show that PFACO has a better tendency of convergence. Compared with ACO, the PFACO algorithm can find a shorter path in same convergence time.
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Zhao Jin AU - Bin Yan AU - Run Ye PY - 2018/03 DA - 2018/03 TI - The Flight Navigation Planning Based on Potential Field Ant Colony Algorithm BT - Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018) PB - Atlantis Press SP - 200 EP - 204 SN - 1951-6851 UR - https://doi.org/10.2991/acaai-18.2018.47 DO - 10.2991/acaai-18.2018.47 ID - Jin2018/03 ER -