Three-dimensional path planning for underwater vehicles based on an improved ant colony optimization algorithm

被引：0
作者：
Yang, L. [1 ]
Li, K.S. [1 ]
Zhang, W.S. [2 ]
Wang, Y. [3 ]
Chen, Y. [1 ]
Zheng, L.F. [1 ]
机构：
[1] College of Mathematics and Informatics, South China Agricultural University, Guangzhou
[2] Institute of Automation, Chinese Academy of Sciences, Beijing
[3] Department of Computer science and information systems, Bradley University, Peoria, 61625, IL
来源：
Journal of Engineering Science and Technology Review | 2015年 / 8卷 / 05期
基金：
中国国家自然科学基金;
关键词：
3D path planning; Ant colony algorithm; Environment modeling; Pheromone exclusion;
D O I：
10.25103/jestr.085.05
中图分类号：
学科分类号：
摘要：
Three-dimensional path planning for underwater vehicles is an important problem that focuses on optimizing the route with consideration of various constraints in a complex underwater environment. In this paper, an improved ant colony optimization (IACO) algorithm based on pheromone exclusion is proposed to solve the underwater vehicle 3D path planning problem. The IACO algorithm can balance the tasks of exploration and development in the ant search path, and enable the ants in the search process to explore initially and develop subsequently. Then, the underwater vehicle can find the safe path by connecting the chosen nodes of the 3D mesh while avoiding the threat area. This new approach can overcome common disadvantages of the basic ant colony algorithm, such as falling into local extremum, poor quality, and low accuracy. Experimental comparative results demonstrate that this proposed IACO method is more effective and feasible in underwater vehicle 3D path planning than the basic ACO model. © 2015 Kavala Institute of Technology.
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收藏
页码：24 / 33
页数：9
相关论文
共 18 条
  • [1] Chen X., Kong Y., Fang X., Wu Q., A fast two-stage ACO algorithm for robotic path planning, Neural Computing and Applications, 22, 2, pp. 313-319, (2013)
  • [2] Ghoseiri K., Nadjari B., An ant colony optimization algorithm for the bi-objective shortest path problem, Applied Soft Computing, 10, 4, pp. 1237-1246, (2010)
  • [3] Qi Y., Wei Z.Q., Yin B., Fei Y.D., Yu Z.D., Zhuang X.D., Enhanced ant colony algorithm for optimal path planning of mobile robot, Journal of Harbin Institute of Technology, 41, 3, pp. 130-133, (2009)
  • [4] Tan G.Z., He H., Sloman A., Ant colony system algorithm for real-time globally optimal path planning of mobile robots, Acta Automatica Sinica, 33, 3, pp. 279-285, (2007)
  • [5] Sun M.X., Wang Y.H., Wu X.M., Feng X.S., Guan Y.L., The underwater robot path planning based on dynamic compensation, Robots, 15, 2, pp. 8-12, (1993)
  • [6] Carroll K.P., McClaran S.R., Nelson E.L., Barnett D.M., Friesen D.K., William G.N., AUV Path Planning: An A* Approach to Path Planning Width Consideration of Variable Vehicle Speeds and Multiple, Overlapping, Time Dependent Exclusion Zones, Proceedings of the Symposium on AUV Technology, pp. 79-84, (1992)
  • [7] Hao Y.L., Zhang J.J., AUV path planning in 3D seabed based on genetic algorithm, China Engineering Science, 5, 1, pp. 56-60, (2003)
  • [8] Chen S.M., Xie J., Chen W.D., Fang J.H., Three-dimensional path planning based on HPSO algorithm, Journal of Huazhong University of Science and Technology, 2, 41, pp. 109-113, (2013)
  • [9] Blum C., Ant colony optimization: Introduction and recent trends, Physics of Life reviews, 2, 4, pp. 353-373, (2005)
  • [10] Narasimha K.V., Kivelevitch E., Sharma B., Kumar M., An ant colony optimization technique for solving min-max multidepot vehicle routing problem, Swarm and Evolutionary Computation, 13, pp. 63-73, (2013)
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