复杂环境下UUV完全遍历路径规划方法

温志文; 杨春武; 蔡卫军; 毛金明

doi:10.11993/j.issn.1673-1948.2017.01.005

复杂环境下UUV完全遍历路径规划方法

doi: 10.11993/j.issn.1673-1948.2017.01.005

1.
中国船舶重工集团公司第705研究所, 陕西西安, 710077
2.
水下信息与控制国家重点实验室, 陕西西安, 710077

详细信息

作者简介:
温志文(1992-), 男, 在读硕士, 主要研究方向为鱼雷总体技术.

中图分类号: TJ630.33; TP24
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出版历程
- 刊出日期: 2017-02-20

A Complete Coverage Path Planning Method of UUV under Complex Environment

1.
The 705 Research Institute, China Shipbuilding Industry Corporation, Xi′an 710077, China
2.
Science and Technology on Underwater Information and Control Laboratory, Xi′an 710077, China

摘要

摘要: 针对复杂环境下无人水下航行器(UUV)完全遍历路径规划方法的不足, 文中基于蚁群算法和生物激励神经网络, 提出了一种高覆盖率、低重复率的完全遍历路径规划方法。该方法基于栅格法和生物激励神经网络进行环境建模, 将神经元活性值引入蚂蚁转移概率公式中, 既克服了蚁群算法需要对环境提前扫描学习, 运算复杂的不足, 又避免了生物激励神经网络随机性强, 重复率高的缺陷。仿真试验表明, 文中方法不仅有效实现了复杂环境下UUV完全遍历路径规划, 而且能够以最短路线跳出死角, 具有覆盖率大、重复率小, 实用性强的优点。该研究可为进一步开展动态环境中的UUV完全遍历路径规划提供参考。
- 无人水下航行器(UUV) /
- 蚁群算法 /
- 生物激励神经网络 /
- 完全遍历路径规划
Abstract: A new method with high coverage rate and low repetition rate is presented to solve the complete coverage path planning problem for an unmanned underwater vehicle(UUV) under complex environment. The method integrates ant colony algorithm and biologically inspired neural network. In this method, environment is modeled with grid cell and biologically inspired neural network, and the neuronic activity is introduced into the ant transition probability formula. As a result, the shortcomings of ant colony algorithm, e.g., scanning and learning environment in advance are needed and computation is complicated, are overcome, and the defects of high complexity, high randomness and high repetition rate of the biologically inspired neural network are avoided. Simulation experiment in complex environment shows that complete coverage path planning of UUV can be efficiently implemented by the proposed method, and the method can get rid of the dead corner in the shortest route. The present method has higher coverage rate and lower repetition rate. This research may provide a reference for further improvement of the UUV complete coverage path planning in dynamic environment.
- unmanned underwater vehicle(UUV) /
- ant colony algorithm /
- biologically inspired neural network /
- complete coverage path planning

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参考文献(7)

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