[1]PU Xingcheng,XIAN Wenjie,NIE Zhuang.Three-dimensional path planning of AUV based on improved ant colony optimization algorithm[J].CAAI Transactions on Intelligent Systems,2024,19(3):627-634.[doi:10.11992/tis.202211038]
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Three-dimensional path planning of AUV based on improved ant colony optimization algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 3 Page number: 627-634 Column: 学术论文—机器人 Public date: 2024-05-05
Title:
Three-dimensional path planning of AUV based on improved ant colony optimization algorithm
Author(s):
PU Xingcheng1; 2;  XIAN Wenjie1;  NIE Zhuang1
1. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
2. School of Mathematics and Computer Science, Tongling University, Tongling 244061, China
Keywords:
path planning; improved ant colony algorithm; heuristic function; pheromone update; convergence speed; three-dimensional path planning; autonomous underwater vehicle; transition probability
CLC:
TP242
DOI:
10.11992/tis.202211038
Abstract:
A new and improved ant colony algorithm is proposed and applied to AUV in 3D path planning. This method addresses the disadvantages of slow convergence and difficulty in achieving the optimum of conventional ant colony algorithms in 3D path planning. Compared with existing algorithms, the improved algorithm mainly has three advantages. First, the pseudorandom state transition probability is introduced to improve the global search ability of the algorithm. Second, the distance and trajectory limitations are considered in the heuristic function, using the distance factor to ensure the search continues to approach the target point. Under the constraint of trajectory limitation, the cumulative rotation angle of the trajectory is small, thereby increasing the convergence speed and accuracy. Finally, the path planning efficiency can be further improved by expanding the incremental gap of pheromones and gradually increasing the attenuation coefficient of pheromones. Test results show that, by using the improved ant colony algorithm, a reduced path of the accumulative turning angle can be obtained, the path length decreases, and the convergence speed accelerates.
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