一种基于改进蚁群优化算法的载人潜水器全局路径规划
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摘要
基础蚁群优化算法在解决复杂障碍环境下的载人潜水器路径规划问题时,易过早收敛于局部最优解,信息素挥发系数的设置过于依靠经验,路径规划结果受概率影响大且不稳定。为此,提出了一种改进蚁群算法用于解决载人潜水器的全局路径规划问题。该算法提出"路径延伸块"的概念。算法前期采用动态更新信息素参数的蚁群优化算法进行简单迭代计算获得原始路径,并对原始路径进行栅格延伸以得到"路径延伸块";后期在路径延伸块中再次使用蚁群算法或其他寻优算法(Dijkstra算法)寻找最优路径。改进的算法与基础蚁群优化算法相比,算法效率及稳定性更高,不易收敛于局部最优解,能更好地适应U型槽环境和复杂障碍环境。
The basic ant colony optimization algorithm can easily converge to the local optimal solution when solving the path planning problem of manned submersibles under the complicated obstacle environment. The pheromone volatility coefficient is set based on experience,and the path planning result is greatly affected by the probability and not stable. Therefore,in this paper,an improved ant colony algorithm is proposed to solve the global path planning problem of manned submersibles. The algorithm proposes the concept of"ath extension block". The algorithm uses an ant colony optimization algorithm that dynamically updates the pheromone parameters to perform simple iterative calculations to obtain the original path,and performs a grid extension on the original path to obtain a "path extension block"; later,the ant colony algorithm or other optimization algorithm is used again in the path extension block( this article takes the Dijkstra algorithm as an example) to find the optimal path. Compared with the basic ant colony optimization algorithm,this algorithm has higher efficiency and stability,and is not easy to converge to the local optimal solution. It can better adapt to U-shaped trough environment and complex obstacle environment.
The basic ant colony optimization algorithm can easily converge to the local optimal solution when solving the path planning problem of manned submersibles under the complicated obstacle environment. The pheromone volatility coefficient is set based on experience,and the path planning result is greatly affected by the probability and not stable. Therefore,in this paper,an improved ant colony algorithm is proposed to solve the global path planning problem of manned submersibles. The algorithm proposes the concept of"ath extension block". The algorithm uses an ant colony optimization algorithm that dynamically updates the pheromone parameters to perform simple iterative calculations to obtain the original path,and performs a grid extension on the original path to obtain a "path extension block"; later,the ant colony algorithm or other optimization algorithm is used again in the path extension block( this article takes the Dijkstra algorithm as an example) to find the optimal path. Compared with the basic ant colony optimization algorithm,this algorithm has higher efficiency and stability,and is not easy to converge to the local optimal solution. It can better adapt to U-shaped trough environment and complex obstacle environment.
引文
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[16]柳长安,鄢小虎,刘春阳,等.基于改进蚁群算法的移动机器人动态路径规划方法[J].电子学报,2011,39(5):1220-1224.(LIU Changan,YAN Xiaohu,LIU Chunyang,et al. Dynamic path planning for mobile robot based on improved ant colony optimization algorithm[J]. Acta Electronica Sinica,2011,39(5):1220-1224.(in Chinese))
[17]史恩秀,陈敏敏,李俊,等.基于蚁群算法的移动机器人全局路径规划方法研究[J].农业机械学报,2014,45(6):53-57.(SHI Enxiu,CHEN Minmin,LI Jun,et al. Research on method of global path-planning for mobile robot based on antcolony algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery,2014,45(6):53-57.(in Chinese))
[18]赵娟平.移动机器人路径规划的蚁群优化算法研究[D].沈阳:东北大学,2012.(ZHAO Juanping. Research on ant colony optimization algorithms for path planning of mobile robot[D]. Shenyang:Northeastern University,2012.(in Chinese))
[19]朱庆保,杨志军.基于变异和动态信息素更新的蚁群优化算法[J].软件学报,2004,15(2):185-192.(ZHU Qingbao,YANG Zhijun. An ant colony optimization algorithm based on mutation and dynamic pheromone updating[J]. Journal of Software,2004,15(2):185-192.(in Chinese))
[20] DORIGO M,GAMBARDELLA L M. Ant colony system:A cooperative learning approach to the traveling salesman problem[J].IEEE Trans on Ec,1997,1(1):53-66.
[2]刘峰.深海载人潜水器的现状与展望[J].工程研究-跨学科视野中的工程,2016,8(2):172-178.(LIU Feng. Technical status and development trend of the deep-sea manned submersible[J]. Journal of Engineering Studies,2016,8(2):172-178.(in Chinese))
[3]刘保华,丁忠军,史先鹏,等.载人潜水器在深海科学考察中的应用研究进展[J].海洋学报,2015,37(10):1-10.(LIU Baohua,DING Zhongjun,SHI Xianpeng,et al. Progress of the application and research of manned submersibles used in deep sea scientific investigations[J]. Haiyang Xuebao,2015,37(10):1-10.(in Chinese))
[4] BOARD N C O S. Future needs in deep submergence science:Occupied and unoccupied vehicles in basic ocean research[M].Washington D C:National Academy Press,2003.
[5] ZHANG F M,MARANI G,SMITH R N,et al. Future trends in marine robotics(TC Spotlight)[J]. Robotics&Automation Magazine IEEE,2015,22(1):14-122.
[6]张同伟,唐嘉陵,李正光,等.蛟龙号载人潜水器在深海精细地形地貌探测中的应用[J].中国科学:地球科学,2018,48(7):947-955.(ZHANG Tongwei, TANG Jialing, LI Zhengguang, et al. Use of the Jiaolong manned submersible for accurate mapping of deep-sea topography and geomorphology[J]. Scientia Sinica,Terrae,2018,48(7):947-955.(in Chinese))
[7]徐玉如,庞永杰,甘永,等.智能水下机器人技术展望[J].智能系统学报,2006,1(1):9-16.(XU Yuru,PANG Yongjie,GAN Yong,et al. AUV-state-of-the-art and prospect[J]. CAAI Transactions on Intelligent Systems,2006,1(1):9-16.(in Chinese))
[8]朱庆保,张玉兰.基于栅格法的机器人路径规划蚁群算法[J].机器人,2005,27(2):132-136.(ZHU Qingbao,ZHANG Yulan. An ant colony algorithm based on grid method for mobile robot path planning[J]. Robot,2005,27(2):132-136.(in Chinese))
[9]孙炜,吕云峰,唐宏伟,等.基于一种改进A*算法的移动机器人路径规划[J].湖南大学学报(自然科学版),2017,44(4):94-101.(SUN Wei,LV Yunfeng,TANG Hongwei,et al. Mobile robot path planning based on an improved A*algorithm[J]. Journal of Hunan University,Natural Sciences,2017,44(4):94-101.(in Chinese))
[10]陈超,唐坚.基于可视图法的水面无人艇路径规划设计[J].中国造船,2013(1):129-135.(CHEN Chao,TANG Jian. A V-graph based path planning for unmanned surface vehicle[J]. Shipbuilding of China,2013(1):129-135.(in Chinese))
[11] GAO B,XU D,ZHANG F,et al. Constructing visibility graph and planning optimal path for inspection of 2D workspace[C]∥Proceedings of the IEEE International Conference on Intelligent Computing and Intelligent Systems. IEEE,2009:693-698.
[12]朱毅,张涛,宋靖雁.非完整移动机器人的人工势场法路径规划[J].控制理论与应用,2010,27(2):152-158.(ZHU Yi,ZHANG Tao,SONG Jingyan. Path planning for nonholonomic mobile robots using artificial potential field method[J].Control Theory&Applications,2010,27(2):152-158.(in Chinese))
[13]范世鹏,祁琪,路坤锋,等.基于改进人工势场法的巡航导弹自主避障技术[J].北京理工大学学报,2018,38(8):828-834.(FAN Shipeng,QI Qi,LU Kunfeng,et al. Autonomous collision avoidance technique of cruise missiles based on modified artificial potential method[J]. Transactions of Beijing Institute of Technology,2018,38(8):828-834.(in Chinese))
[14] QI N N,MA B J,LIU X E,et al. A modified artificial potential field algorithm for mobile robot path planning[C]∥Proceedings of the Intelligent Control and Automation World Congress. 2008:2603-2607.
[15] BOTEE H M,BONABEAU E. Evolving ant colony optimization[J]. Advances in Complex Systems,1998,1(02n03):149-159.
[16]柳长安,鄢小虎,刘春阳,等.基于改进蚁群算法的移动机器人动态路径规划方法[J].电子学报,2011,39(5):1220-1224.(LIU Changan,YAN Xiaohu,LIU Chunyang,et al. Dynamic path planning for mobile robot based on improved ant colony optimization algorithm[J]. Acta Electronica Sinica,2011,39(5):1220-1224.(in Chinese))
[17]史恩秀,陈敏敏,李俊,等.基于蚁群算法的移动机器人全局路径规划方法研究[J].农业机械学报,2014,45(6):53-57.(SHI Enxiu,CHEN Minmin,LI Jun,et al. Research on method of global path-planning for mobile robot based on antcolony algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery,2014,45(6):53-57.(in Chinese))
[18]赵娟平.移动机器人路径规划的蚁群优化算法研究[D].沈阳:东北大学,2012.(ZHAO Juanping. Research on ant colony optimization algorithms for path planning of mobile robot[D]. Shenyang:Northeastern University,2012.(in Chinese))
[19]朱庆保,杨志军.基于变异和动态信息素更新的蚁群优化算法[J].软件学报,2004,15(2):185-192.(ZHU Qingbao,YANG Zhijun. An ant colony optimization algorithm based on mutation and dynamic pheromone updating[J]. Journal of Software,2004,15(2):185-192.(in Chinese))
[20] DORIGO M,GAMBARDELLA L M. Ant colony system:A cooperative learning approach to the traveling salesman problem[J].IEEE Trans on Ec,1997,1(1):53-66.
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