UUV 3D Path Planning Based on PSO-ACO Fusion Algorithm

• Published in: 2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC) pp. 599 - 604
• Main Authors: Chen, Yanliang, Luo, Wenhui, Wang, Min, Su, Yixin, Zhang, Huajie
• Format: Conference Proceeding
• Language: English
• Published: IEEE 19.11.2022
• Subjects: Ant colony optimization; Energy consumption; Heuristic algorithms; Mathematical models; particle swarm optimization; Path planning; Solid modeling; Three-dimensional displays; UUV
• DOI: 10.1109/YAC57282.2022.10023579

In order to solve the problems of difficult convergence and local optimal solution of ant colony optimization (ACO) algorithm, and low convergence accuracy of particle swarm optimization (PSO) algorithm, a particle swarm optimization ant colony optimization (PSO-ACO) fusion algorithm is proposed to deal with the three-dimensional (3D) path planning problem of unmanned underwater vehicle (UUV). In this algorithm: based on the idea of spatial stratification, a 3D grid model is established to build underwater environment model; PSO algorithm is used to pre search the path and quickly obtain the solution, which is used as the initial pheromone increment of ACO algorithm; the pheromone global updating method of ACO algorithm is improved: an adjusting factor is added to the pheromone global update equation to accelerate the convergence speed of ACO algorithm; the state transition equation of ACO algorithm is also improved, so that the algorithm has a greater probability to select the point with the largest weighted product of pheromone and heuristic information as the next path point. Experimental results show that the fusion algorithm effectively improves the global search ability and shortens the search time.