Page 94 - Read Online

Mai et al. Intell Robot 2023;3(4):466-84 Intelligence & Robotics
DOI: 10.20517/ir.2023.37

Research Article Open Access

UAV path planning based on a dual-strategy ant colony
optimization algorithm

Xiaoming Mai , Na Dong , Shuai Liu , Hao Chen 1
1
1
1
1 School of Electrical and Information Engineering, Tianjin University, Tianjin 300110, China.
Correspondence to: Prof. Na Dong, School of Electrical and Information Engineering, Tianjin University, No. 92, Weijin Road,
Nankai District, Tianjin 300110, China. E-mail: dongna@tju.edu.cn
How to cite this article: Mai X, Dong N, Liu S, Chen H. UAV path planning based on a dual-strategy ant colony optimization
algorithm. Intell Robot 2023;3(4):466-84. http://dx.doi.org/10.20517/ir.2023.37
Received: 20 Oct 2023 First Decision: 16 Nov 2023 Revised: 28 Nov 2023 Accepted: 8 Dec 2023 Published: 21 Dec 2023

Academic Editor: Haibin Duan, Simon X. Yang Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang

Abstract
With the rapid development of modern communication and automatic control technologies, unmanned aerial vehicles
(UAVs) have increasingly gained importance in both military and civilian domains. Path planning, a critical aspect for
achieving autonomous aerial navigation, has consistently been a focal point in UAV research. However, traditional ant
colony algorithms need to be improved for the drawbacks of susceptibility to local optima and weak convergence capa-
bilities. Consequently, a novel path planning methodology is proposed based on a dual-strategy ant colony algorithm.
In detail, an improved state transition probability rule is introduced, redefining ant movement rules by integrating the
state transition strategy of deterministic selection during the iterative process. Additionally, heuristic information on
adjacent node distance and mountain height is added to further improve the search efficiency of the algorithm. Then,
a new dynamically adjusted pheromone update strategy is proposed. The update strategy is continuously adjusted
during the iteration process, which is beneficial to the algorithm’s global search in the early stage and accelerated
convergence in the later stage, preventing the algorithm from falling into local optimality and improving its conver-
gence. Based on the above improvements, a new variation of ant colony optimization (ACO) called dual-strategy
ACO algorithm is formed. Experimental results prove that dual-strategy ACO has superior global search capabilities
and convergence characteristics from four key aspects: path length, fitness values, iteration number, and running
time.

Keywords: Pathplanning, ant colonyoptimization algorithm, heuristic information, dynamic adjustment of pheromones

© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, shar-
ing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you
give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate
if changes were made.

www.intellrobot.com