In trajectory planning space, the dynamic programming algorithm is characterized by capabilities of computing and optimization. However, in 3D trajectory planning, as the planning space expands, the number of grid points increases faster, and time complexity of the dynamic programming algorithm is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O\left( {n^{3} } \right)$$\end{document}. It often leads to a “Curse of dimensionality” phenomenon, which lowers its computational efficiency drastically. To solve this problem, this paper divides the entire planning space into stages based on Bellman's optimality principle. A dynamic programming-ant colony algorithm is proposed by using ant colony algorithm in each stage for optimization, and dynamic programming algorithm for global planning. The global optimization ability of the algorithm is verified through its convergence analysis. Moreover, based on a comparative analysis of simulation experiments, it shows that the improved algorithm proposed in this paper is more efficient than the dynamic programming algorithm and ant colony algorithm alone, and can be used to plan a global optimal solution.
3D Trajectory Planning of Underwater Vehicle Based on DPACO Algorithm
Lect. Notes Electrical Eng.
2021-10-30
11 pages
Article/Chapter (Book)
Electronic Resource
English
3D Trajectory Planning of Underwater Vehicle Based on DPACO Algorithm
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