The Flight Navigation Planning Based on Potential Field Ant Colony Algorithm

DOI

10.2991/acaai-18.2018.47How to use a DOI?

Keywords

path planning; Ant Colony Optimal; Artificial Potential Field; UAV

Abstract

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.

Copyright

© 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/).

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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  -