基于改进细菌觅食优化的无人艇自主避碰算法

曾小龙; 茅云生; 宋利飞; 董早鹏; 包涛

doi:10.16411/j.cnki.issn1006-7736.2018.04.006

大连海事大学学报 >

2018 , Vol. 44 >Issue 4: 35 - 42

DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2018.04.006

船舶与海洋工程

基于改进细菌觅食优化的无人艇自主避碰算法

曾小龙 ,
茅云生 ,
宋利飞 ,
董早鹏 ,
包涛

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(武汉理工大学高性能船舶技术教育部重点实验室/交通学院，武汉 430063)

曾小龙（1994—），男，硕士生，E-mail：whutzxiaolong@163.com.

收稿日期: 2018-05-02

修回日期: 2018-08-03

网络出版日期: 2018-08-03

基金资助

国家自然科学基金青年科学基金项目（51809203;51709214);武汉理工大学自主创新基金（2017IVA006;2017IVA008）.

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Automatic collision avoidance algorithm for unmanned surface vehicle based on improved bacterial foraging optimization

ZENG Xiao-long ,
MAO Yun-sheng ,
SONG Li-fei ,
DONG Zao-peng ,
BAO Tao

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（Key Laboratory of High Performance Ship Technology Ministry of Education/ Transportation Institute，Wuhan University of Technology，Wuhan 430063，China）

Received date: 2018-05-02

Revised date: 2018-08-03

Online published: 2018-08-03

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摘要

针对无人艇的避碰规划问题，设计一种基于改进细菌觅食优化（BFO）的自主避碰算法. 针对基本BFO算法收敛速度慢、寻优精度低、稳定性低的不足，设计自适应递减分维趋化步长代替固定步长以实现步长的自适应调整，提出优适探寻游动方法解决基本BFO算法无效游动与重复游动的缺陷，设计自适应迁徙概率代替固定迁徙概率以解决基本BFO算法可能导致精英个体丢失的情况. 函数测试仿真表明，改进BFO算法具有更好的收敛速度、寻优精度以及稳定性.改进算法应用于无人艇避碰仿真结果证明，该改进算法能够快速安全地实现无人艇在动态障碍下的自主避碰.

关键词： 无人艇(USV); 自主避碰; 改进细菌觅食优化算法; 自适应递减分维趋化步长; 优适探寻游动; 自适应迁徙

本文引用格式

曾小龙 , 茅云生 , 宋利飞 , 董早鹏 , 包涛 . 基于改进细菌觅食优化的无人艇自主避碰算法[J]. 大连海事大学学报, 2018 , 44(4) : 35 -42 . DOI: 10.16411/j.cnki.issn1006-7736.2018.04.006

Abstract

To solve the collision avoidance planning problem of unmanned surface vehicle（USV）, an automatic collision avoidance algorithm was designed based on improved bacterial foraging optimization（BFO）. Aiming at the shortcomings of slow convergence speed, low optimization precision and low stability resulted from basic BFO, an adaptive diminishing fractal dimension chemotactic step length was designed to replace the fixed step length in order to realize the adaptive adjustment of step length. The optimal search method was put forward to solve the defects of ineffective swimming and repeated swimming in the basic BFO algorithm. An adaptive migration probability was designed instead of fixed migration probability to solve the problem of elite individual loss caused by basic BFO algorithm. The function test simulation shows that the improved BFO algorithm has better convergence speed, better optimization precision and better stability. The improved algorithm was applied into USV collision avoidance simulation, and results show that the improved algorithm can quickly and safely realize the autonomous collision avoidance of UAV under dynamic obstacles.

Key words： unmanned surface vehicle（USV）; autonomous collision avoidance; improved bacterial foraging optimization algorithm; adaptive diminishing fractal dimension chemotactic step length; optimal search for moving; adaptive migration

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