An improved ant colony optimization algorithm based on particle swarm optimization algorithm for path planning of autonomous underwater vehicle

• Published in: Journal of ambient intelligence and humanized computing Vol. 11; no. 8; pp. 3349 - 3354
• Main Authors: Che, Gaofeng, Liu, Lijun, Yu, Zhen
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2020; Springer Nature B.V
• Subjects: Algorithms; Ant colony optimization; Artificial Intelligence; Autonomous underwater vehicles; Collision avoidance; Computational Intelligence; Design; Engineering; Genetic algorithms; Heuristic; Motion control; Optimization algorithms; Original Research; Particle swarm optimization; Path planning; Robotics and Automation; Undersea; User Interfaces and Human Computer Interaction; ACO algorithm; Pheromone; AUV; Path planning; Path point; PSO algorithm
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-019-01531-8

The motion control of autonomous underwater vehicle (AUV) has got more and more attention because AUV has been used in many applications in recent years. In order to find the optimal path for AUV to reach the specified destination in complex undersea environment, an improved ant colony optimization (ACO) algorithm based on particle swarm optimization (PSO) algorithm is proposed. Due to the various constraints, such as the limited energy and limited visual distance, the improved ACO algorithm uses improved pheromone update rule and heuristic function based on PSO algorithm to make AUV find the optimal path by connecting the chosen nodes of the undersea environment while avoiding the collision with the complex undersea terrain (static obstacles). The improved ACO algorithm based on PSO algorithm can overcome disadvantages of the traditional ACO algorithm, such as falling into local extremum, poor quality, and low accuracy. Experiment results demonstrate that improved ACO algorithm is more effective and feasible in path planning for autonomous underwater vehicle than the traditional ant colony algorithm.