采用能耗最优改进蚁群算法的自治水下机器人路径优化

刘贵杰; 刘鹏; 穆为磊; 王寿军

doi:10.7652/xjtuxb201610014

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采用能耗最优改进蚁群算法的自治水下机器人路径优化

更新时间：2025-07-09

- 采用能耗最优改进蚁群算法的自治水下机器人路径优化
- A Path Optimization Algorithm for AUV Using an Improved Ant Colony Algorithm with Optimal Energy Consumption
- 西安交通大学学报 2016年50卷第10期页码：93-98
- 作者机构：
  
  中国海洋大学机电工程系,山东,青岛,266100
- 作者简介：
- 基金信息：
  
  : 2016-03-15作者简介: 刘贵杰(1968—),男,教授;穆为磊(通信作者),男,讲师。基金项目: 国家自然科学基金资助项目(61540010,61501418)
- DOI：10.7652/xjtuxb201610014
  中图分类号： TP242
- 网络首发：2016-10-10，
  
  纸质出版：2016
- 稿件说明：
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刘贵杰, 刘鹏, 穆为磊, 等. 采用能耗最优改进蚁群算法的自治水下机器人路径优化[J]. 西安交通大学学报, 2016,50(10):93-98.

A Path Optimization Algorithm for AUV Using an Improved Ant Colony Algorithm with Optimal Energy Consumption[J]. 2016, 50(10): 93-98.
刘贵杰, 刘鹏, 穆为磊, 等. 采用能耗最优改进蚁群算法的自治水下机器人路径优化[J]. 西安交通大学学报, 2016,50(10):93-98. DOI： 10.7652/xjtuxb201610014.

A Path Optimization Algorithm for AUV Using an Improved Ant Colony Algorithm with Optimal Energy Consumption[J]. 2016, 50(10): 93-98. DOI： 10.7652/xjtuxb201610014.

摘要

针对传统路径优化算法中“距离最短能耗非最低”的问题

提出了一种基于能耗最优改进蚁群算法的自治水下机器人路径优化算法。该算法通过对水下机器人进行水动力学分析

建立了水下机器人移动过程中的受力模型; 得到了机器人移动路径的能耗计算公式; 提出了能耗最优的改进蚁群算法

采用路径能耗的倒数作为路径信息素值

实现了能耗指导蚁群进化的目的。实验结果表明:该算法规划的路径长433.51 m

水下机器人能耗12 235.17 J

算法寻优迭代次数22次; 传统距离最优算法规划的路径长393.56 m

水下机器春能耗12 864.99 J

算法寻优迭代次数33次。该算法规划的路径距离虽比传统算法长10%

但是能耗却降低了5%

收敛速度明显比传统算法快

对降低水下机器人能耗、提高续航能力有一定的优势。

Abstract

A path optimization algorithm for autonomous underwater vehicles is proposed to solve the problem of the shortest distance with not the lowest energy consumption in the traditional path optimization algorithm. The algorithm bases on an improved ant colony algorithm with optimal energy consumption. A stress model of movement process of AUV in water is built by studying the hydrodynamic analysis of AUV and a formula to calculate the energy consumption of AUV moving path is derived. Then

the improved ant colony algorithm with optimal energy consumption is presented. The inverse of the path energy consumption is used as a path pheromone value to guide evaluation of ant colonies by energy consumption. Experimental results show that the proposed algorithm uses 22 iterations to plan a 433.51 m long path with 12 235.17 J AUV energy consumption

while a traditional algorithm uses 33 iterations to plan a393.65 m long path with 12 864.99 J AUV energy consumption. The path distance planned by the proposed algorithm is 10% longer than that planned by the traditional algorithm

but it's energy consumption is lower by 5%. It is clear that the algorithm has advantages of reducing the energy consumption and improving the battery life.

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references

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