基于海图和改进粒子群优化算法的AUV全局路径规划

doi:10.13973/j.cnki.robot.190100

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张岳星, 王轶群, 李硕, 王晓辉. 基于海图和改进粒子群优化算法的AUV全局路径规划[J]. 机器人, 2020, 42(1): 120-128.DOI: 10.13973/j.cnki.robot.190100. ZHANG Yuexing, WANG Yiqun, LI Shuo, WANG Xiaohui. Global Path Planning for AUV Based on Charts and the Improved Particle Swarm Optimization Algorithm. ROBOT, 2020, 42(1): 120-128. DOI: 10.13973/j.cnki.robot.190100.

摘要针对AUV（自主水下机器人）在复杂条件海域做全局路径规划时面临的环境信息缺少，环境建模困难和常规算法复杂、求解能力弱等问题，提出一种基于海图和改进粒子群优化算法的全局路径规划方法．首先利用电子海图的先验知识建立3维静态环境模型，并构造路径航程、危险度和平滑函数；在粒子群优化算法中引入搜索因子和同性因子自适应地调整参数，并结合鱼群算法的“跳跃”过程提升算法的求解能力．同时建立安全违背度和选优规则以提高所规划路径的安全性．仿真实验结果表明，本文方法与传统粒子群算法和蚁群算法相比，规划出短航程、安全性高的全局路径的能力更强，可满足AUV在复杂海域航行时的全局路径规划需求．

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关键词 ：自主水下机器人, 全局路径规划, 电子海图, 粒子群优化（PSO）算法

Abstract：When the AUV (autonomous underwater vehicle) plans a global path in sea area with complex conditions, there exist various problems, such as the lack of environmental information, the difficulty in environmental modeling, and the high complexity and the weak solution ability of conventional algorithms. To solve the problems, a global path planning method based on charts and the improved PSO (particle swarm optimization) algorithm is proposed. Firstly, a 3-dimensional static environment model based on the prior knowledge of electronic charts, and functions of the path range, the hazard degree and the smoothness are constructed. In order to improve the solution ability of the algorithm, the search factor and homology factor are introduced into the PSO algorithm to adjust the parameters adaptively, and the jump process of the fish swarm algorithm is also combined. Meanwhile, the security violation degree and the optimization rules are established to improve the security of the planned path. Finally, the simulation results show that the proposed method is more capable of planning short-range and high-security global paths compared with the traditional PSO algorithms and the ant colony algorithm (ACA), and can meet the requirements of AUV global path planning in complex sea conditions.

Key words： AUV (autonomous underwater vehicle) global path planning electronic chart particle swarm optimization (PSO) algorithm

收稿日期: 2019-03-06 录用日期: 2019-09-27

:	TP242
	TP182

基金资助:国家重点研发计划（2017YFC0305700）

通讯作者: 张岳星,zhangyuexing@sia.cn E-mail: zhangyuexing@sia.cn

作者简介: 张岳星(1992-),男,博士生.研究领域:水下机器人.
王轶群(1985-),男,博士生.研究领域:水下机器人导航与控制.
李硕(1970-),男,博士,研究员,博士生导师.研究领域:水下自主遥控机器人,极地机器人.

链接本文:

https://robot.sia.cn/CN/10.13973/j.cnki.robot.190100 或 https://robot.sia.cn/CN/Y2020/V42/I1/120

[1] Zhuang Y F, Sharma S, Subudhi B, et al. Efficient collision-free path planning for autonomous underwater vehicles in dynamicenvironments with a hybrid optimization algorithm[J]. Ocean Engineering, 2016, 127:190-199.
[2] Li D, Zheng Z, Meng X, et al. Robot path planning based on an improved multi-objective PSO method[C]//International Conference on Computer Engineering, Information Science & Application Technology. Paris, France:Atlantis Press, 2016:496-501.
[3] Zhang Y, Gong D W, Zhang J H. Robot path planning in uncertain environment using multi-objective particle swarm optimization[J]. Neurocomputing, 2013, 103:172-185.
[4] Arora J. Introduction to optimum design[M]. 4th ed. Cambridge, USA:Academic Press, 2017:707-738.
[5] Tang J, Zhu J H, Sun Z Q. A novel path planning approach based on AppART and particle swarm optimization[M]//Lecture Notes in Computer Science, Vol.3498. Berlin, Germany:Springer, 2005:253-258.
[6] Javidrad F, Nazari M. A new hybrid particle swarm and simulated annealing stochastic optimization method[J]. Applied Soft Computing, 2017, 60:634-654.
[7] Garcia M A P, Montiel O, Castillo O, et al. Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation[J]. Applied Soft Computing, 2009, 9(3):1102-1110.
[8] Dadgar M, Jafari S, Hamzeh A. A PSO-based multi-robot cooperation method for target searching in unknown environments[J]. Neurocomputing, 2016, 177:62-74.
[9] Tang B W, Zhu Z X, Luo J J. Hybridizing particle swarm optimization and differential evolution for the mobile robot global path planning[J]. International Journal of Advanced Robotic Systems, 2016, 13:13-86.
[10] Su Y X, Chi R. Multi-objective particle swarm-differential evolution algorithm[J]. Neural Computing and Applications, 2017, 28(2):1-12.
[11] Chen Y G, Li L X, Peng H P, et al. Particle swarm optimizerwith two differential mutation[J]. Applied Soft Computing, 2017, 61:314-330.
[12] Panda A, Mallipeddi R, Das S. Particle swarm optimization with a modified learning strategy and blending crossover[C]//IEEE Symposium Series on Computational Intelligence. Piscataway, USA:IEEE, 2018:415-422.
[13] Nie Z B, Yang X B, Gao S H, et al. Research on autonomous moving robot path planning based on improved particle swarm optimization[C]//IEEE Congress on Evolutionary Computation. Piscataway, USA:IEEE, 2016:2532-2536.
[14] Li S F, Cheng C Y. Particle swarm optimization with fitness adjustment parameters[C]//Computers & Industrial Engineering. Oxford, UK:Elsevier, 2017:831-841.
[15] Chatterjee A, Siarry P. Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization[J]. Computers & Operations Research, 2006, 33(3):859-871.
[16] Xu G. An adaptive parameter tuning of particle swarm optimization algorithm[J]. Applied Mathematics and Computation, 2013, 219(9):4560-4569.
[17] Mac T T, Copot C, Tran D T, et al. A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization[J]. Applied Soft Computing, 2017, 59:68-76.
[18] 刘勇,贾庆轩,陈钢,等.基于多目标粒子群优化算法的自由漂浮空间机器人负载最大化轨迹优化[J].机器人,2014,36(4):402-410. Liu Y, Jia Q X, Chen G, et al. Load maximization trajectory optimization for free-floating space robot using multi-objective[J]. Robot, 2014, 36(4):402-410.
[19] 霍本岩,赵新刚,韩建达,等.基于多目标粒子群优化算法的斜尖柔性针穿刺路径规划[J].机器人,2015,37(4):385-394. Huo B Y, Zhao X G, Han J D, et al. Puncture path planning for bevel-tip flexible needle based on multi-objective particle swarm optimization algorithm[J]. Robot, 2015, 37(4):385-394.
[20] Yan Z P, He J W, Li J. An improved multi-AUV patrol path planning method[C]//IEEE International Conference on Mechatronics and Automation. Piscataway, USA:IEEE, 2017:1930-1936.
[21] Shen Z Y, Song J N, Mittal K, et al. An autonomous integrated system for 3-D underwater terrain map reconstruction[C]// OCEANS 2016. Piscataway, USA:IEEE, 2016. DOI:10.1109/OCEANS.2016.7761067.
[22] Said N M, Mahmud M R, Hasan R C. Satellite-derived bath-ymetry:Accuracy assessment on depths derivation algorithm for shallow water area[C]//International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017:159-164.
[23] 邬伦,刘瑜.地理信息系统原理、方法和应用[M].北京:科学出版社,2001:178-192. Wu L, Liu Y. Geographic information system:Principle, me-thod and application[M]. Beijing:Science Press, 2001:178-192.
[24] 刘金坤.智能控制[M].4版.北京:电子工业出版社,2017:24. Liu J K. Intelligent control[M]. 4th ed. Beijing:Publishing House of Electronics Industry, 2017:24.
[25] Poli R, Kennedy J, Blackwell T. Particle swarm optimization[J]. Swarm Intelligence, 2007, 1(1):33-57.
[26] Trelea I C. The particle swarm optimization algorithm:Convergence analysis and parameter selection[J]. Information Processing Letters, 2003, 85(6):317-325.
[27] Zhang Y, Guan G L, Pu X C. The robot path planning based on improved artificial fish swarm algorithm[J]. Mathematical Problems in Engineering, 2016. DOI:10.1155/2016/3297585.
[28] 温正,孙华克.MATLAB智能算法[M].北京:清华大学出版社,2017:309-315.Wen Z, Sun H K. MATLAB intelligent algorithm[M]. Beijing:Qsinghua University Press, 2017:309-315.