基于改进细菌觅食优化的无人艇自主避碰算法
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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.
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.
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.
引文
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[15]陈超,耿沛文,张新慈.基于改进人工势场法的水面无人艇路径规划研究[J].船舶工程,2015(9):72-75.CHEN C,GENG P W,ZHANG X C,et al. Path planning for surface unmanned vehicle based on improved artificial potential field method[J]. Ship Engineering,2015(9):72-75.(in Chinese)
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[17]任鹏.基于船舶碰撞危险度的避碰决策研究[D].大连:大连海事大学,2015.REN P. Research on collision avoidance decision based on collision risk of ship[D]. Dalian:Dalian Maritime University,2015.(in Chinese)
[18]祖伟.基于粒子群优化算法的水下潜器实时路径规划技术研究[D].哈尔滨:哈尔滨工程大学,2008.ZHU W. Research on real time path planning technology of underwater vehicle based on particle swarm optimization algorithm[D]. Harbin:Harbin Engineering University,2008.(in Chinese)
[2]董早鹏,万磊,宋利飞,等.基于自适应专家S面算法的微小型USV控制系统设计[J].中国造船,2017,58(2):178-188.DONG Z P,WAN L,SONG L F,et al. Design of control system for micro-USV based on adaptive expert S plane algorithm[J]. Ship Building of China,2017,58(2):178-188.(in Chinese)
[3]SARDA E I,QU H,BERTASKA I R,et al. Stationkeeping control of an unmanned surface vehicle exposed to current and wind disturbances[J]. Ocean Engineering,2016,127:305-324.
[4]柳晨光,初秀民,吴青,等. USV发展现状及展望[J].中国造船,2014(4):194-205.LIU C G,CHU X M,WU Q,et al. Development status and prospect of USV[J]. Ship Building of China,2014(4):194-205.(in Chinese)
[5]MEI J H,ARSHAD M R. A hybrid artificial potential field method for autonomous surface vessel path planning in dynamic riverine environment[J]. Indian Journal of Geo-Marine Sciences,2015,44(11):1678-1683.
[6]WATANABE Y,CALISE A J,JOHNSON E N. Visionbased obstacle avoidance for UAVs[C]//AIAA Guidance,Navigation and Control Conference and Exhibit.Hilton Head,South Carolina:[s. n.],2007:1-11.
[7]KUWATA Y,WOLF M T,ZARZHITSKY D,et al. Safe maritime autonomous navigation with COLREGS,using velocity obstacles[J]. IEEE Journal of Oceanic Engineering,2011,39(1):110-119.
[8]茅云生,宋利飞,向祖权,等.水面无人艇多船障碍智能避碰[J].大连海事大学学报,2015,41(4):8-13.MAO Y S,SONG L F,XIANG Z Q,et al. Multi-obstacle intelligent collision avoidance strategy for unmanned surface vehicle[J]. Journal of Dalian Maritime University,2015,41(4):8-13.(in Chinese)
[9]SONG C H. Global path planning method for USV system based on improved ant colony algorithm[J]. Applied Mechanics&Materials,2014,568-570:785-788.
[10]吴博,熊勇,文元桥.基于速度障碍原理的无人艇自动避碰算法[J].大连海事大学学报,2014,40(2):13-16.WU B,XIONG Y,WEN Y Q. Automatic collision avoidance algorithm for unmanned surface vessel based on velocity obstacles[J]. Journal of Dalian Maritime University,2014,40(2):13-16.(in Chinese)
[11]ZHANG R,TANG P,SU Y,et al. An adaptive obstacle avoidance algorithm for unmanned surface vehicle in complicated marine environments[J]. IEEE/CAA Journal of Automatica Sinica,2014,1(4):385-396.
[12]刘珍,孙京诰.一种改进的细菌觅食优化算法[J].华东理工大学学报(自然科学版),2016,42(2):225-232.ZHEN L,SUN J G. An improved bacterial foraging optimization algorithm[J]. Journal of East China University of Science&Technology(Natural Science Edition),2016,42(2):225-232.(in Chinese)
[13]包敏.混合差分和细菌觅食的多目标优化算法[D].兰州:兰州交通大学,2016.BAO M. Multi objective optimization algorithm for mixed differential and bacterial foraging[D]. Lanzhou:Lanzhou Jiaotong University,2016.(in Chinese)
[14]尚明栋,朱志宇,周涛.基于改进蚁群算法的水面无人艇智能避碰方法研究[J].船舶工程,2016(9):6-9.SHANG M D,ZHU Z Y,ZHOU T,et al. Intelligent collision avoidance method for surface unmanned vehicle based on improved ant colony algorithm[J]. Ship Engineering,2016(9):6-9.(in Chinese)
[15]陈超,耿沛文,张新慈.基于改进人工势场法的水面无人艇路径规划研究[J].船舶工程,2015(9):72-75.CHEN C,GENG P W,ZHANG X C,et al. Path planning for surface unmanned vehicle based on improved artificial potential field method[J]. Ship Engineering,2015(9):72-75.(in Chinese)
[16]LIU Y,SONG R,BUCKNALL R. A practical path planning and navigation algorithm for an unmanned surface vehicle using the fast marching algorithm[C]//Oceans.[S. l.]:IEEE,2015:1-7.
[17]任鹏.基于船舶碰撞危险度的避碰决策研究[D].大连:大连海事大学,2015.REN P. Research on collision avoidance decision based on collision risk of ship[D]. Dalian:Dalian Maritime University,2015.(in Chinese)
[18]祖伟.基于粒子群优化算法的水下潜器实时路径规划技术研究[D].哈尔滨:哈尔滨工程大学,2008.ZHU W. Research on real time path planning technology of underwater vehicle based on particle swarm optimization algorithm[D]. Harbin:Harbin Engineering University,2008.(in Chinese)
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