| 一种基于改进蚁群算法的载人潜水器全局路径规划 |
| A Global Path Planning of Manned Submersible Based on Improved Ant Colony Algorithm |
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| DOI： |
| 中文关键词: 载人潜水器，路径规划，蚁群算法，人工势场法 |
| 英文关键词:manned submersible path planning ant colony optimization algorithm artificial potential field method |
| 基金项目: |
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| 中文摘要: |
| 当前关于使用蚁群算法解决载人潜水器路径规划问题的研究，往往只注重路径的长度和算法收敛速度，容易忽略路径点与障碍物之间的距离和路径的平滑度等要素。载人潜水器过于靠近障碍物航行时，容易产生碰撞；按照不平滑路径行驶时，频繁地转向会降低航行效率。为解决这些问题，本文受人工势场法启发，在蚁群算法的概率选择环节引入障碍物惩罚因子和转向惩罚因子，对路径点的选择加以限制。仿真测试表明，相比于传统蚁群算法和Dijkstra算法，本算法规划的路径与障碍物之间保持安全距离且转向次数更少。载人潜水器按照此路径航行时，安全性和航行效率更高。 |
| 英文摘要: |
| Current research on ant colony algorithm (ACO) in solving the path planning problem of manned submersible often pays attention to the length of the path and the convergence speed of the algorithm, but easily ignores the distance between the path points and obstacles and the smoothness of the path. When a manned submersible is too close to an obstacle to navigate, it is prone to collision; when driving on an unsmooth path, frequent steering will reduce navigation efficiency. In order to solve these problems, this paper combines the artificial potential field method to introduce the obstacle penalty factor and the steering penalty factor in the probability selection of the ant colony algorithm to limit the choice of path points. Simulation tests show that compared with Dijkstra algorithm and traditional ant colony algorithm, the proposed algorithm is far away from obstacles and has fewer turns. Manned submersibles are safer and more efficient when sailing on this route. |
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