基于行为策略的AUV全覆盖信度函数路径规划算法

doi:10.16182/j.issn1004731x.joss.201805031

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (5): 1857-1868.doi: 10.16182/j.issn1004731x.joss.201805031

基于行为策略的AUV全覆盖信度函数路径规划算法

甘文洋, 朱大奇

上海海事大学水下机器人与智能系统实验室,上海201306

收稿日期:2016-06-20 修回日期:2016-10-19 发布日期:2019-01-03
通讯作者: 朱大奇(通讯作者1964-),安徽安庆,博士,教授,研究方向为水下机器人控制与路径规划。
作者简介:甘文洋(1992-),女,安徽安庆,博士生,研究方向为AUV自适应全覆盖路径规划。
基金资助:
国家自然科学基金(51279098,51575336),上海市科委创新行动计划(13510721400)

Complete Coverage Belief Function Path Planning Algorithm of Autonomous Underwater VehicleBased on Behavior Strategy

Gan Wenyang, Zhu Daqi

Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai 201306, China.

Received:2016-06-20 Revised:2016-10-19 Published:2019-01-03

摘要/Abstract

摘要： 针对自治水下机器人全覆盖路径规划问题,通过引入行为策略,结合地图信度函数概念,提出一种基于行为策略的全覆盖信度函数路径规划算法,使AUV完成全覆盖任务的同时自动避开障碍物。根据环境信息构建栅格信度函数,用不同的函数值区分障碍物、已覆盖和未覆盖栅格;AUV根据路径规划策略选择下一步航行位置,若AUV航行到障碍物边缘,则利用行为策略选择下一步航行位置,否则使用栅格信度函数选择下一步航行位置。通过二维和三维仿真实验证明了所提算法不仅能够完成全覆盖任务,同时减少了AUV陷入死区的次数,降低了遍历重复率。

关键词: 自治水下机器人, 全覆盖路径规划, 信度函数, 行为策略

Abstract: For the complete coverage path planning of autonomous underwater vehicle (AUV), a complete coverage belief function path planning algorithm based on behavior strategy is proposed by introducing the behavior strategy and using the concept of map belief function, which can make AUV accomplish the complete coverage and avoid obstacles automatically. The grid belief function is constructed based on environmental information, using different function values to distinguish between the obstacle, the covered and the uncovered grid. AUV selects the next navigation position by path planning strategy. Next navigation position is selected by behavior strategy if AUV navigates to the edge of obstacle. Otherwise, belief function is used to select navigation position of AUV. By simulation experiments in 2-D and 3-D environment, the algorithms mentioned in this paper is proved to be capable of accomplishing the complete coverage, decreasing the number of AUV into dead zone and reducing the overlay repetition rate.

Key words: autonomous underwater vehicle (AUV), complete coverage path planning, belief function, behavior strategy

中图分类号:

TP273

甘文洋, 朱大奇. 基于行为策略的AUV全覆盖信度函数路径规划算法[J]. 系统仿真学报, 2018, 30(5): 1857-1868.

Gan Wenyang, Zhu Daqi. Complete Coverage Belief Function Path Planning Algorithm of Autonomous Underwater VehicleBased on Behavior Strategy[J]. Journal of System Simulation, 2018, 30(5): 1857-1868.

参考文献

[1] 简毅, 张月. 移动机器人全局覆盖路径规划算法研究进展与展望[J]. 计算机应用, 2014, 34(10): 2844-2849.
Jian Yi, Zhang Yue.Complete coverage path planning algorithm for mobile robot: progress and prospect[J]. Journal of Computer Applications, 2014, 34(10): 2844-2849.
[2] 杨勇. 基于混合算法的移动机器人路径规划研究[J]. 微处理机, 2015(1): 44-46.
Yang Yong.Research on Path Planning of Mobile Robot Based on Hybrid Algorithm[J]. Microprocessors, 2015(1): 44-46.
[3] Kapanoglu M, Alikalfa M, Ozkan M, et al.A pattern based genetic algorithm for multi-robot coverage path planning minimizing completion time[J]. Journal of Intelligent Manufacturing (S0956-5515), 2012, 23(4): 1035-1045.
[4] Hofner C, Schmidt G.Path planning and guidance techniques for an autonomous mobile cleaning robot[J]. Robotics and Autonomous Systems (S0921-8890), 1995, 14(2): 199-212.
[5] Schmidt G, Hofner C.An advanced planning and navigation approach for autonomous cleaning robot operation[C]// IEEE International Conference on Intelligent Robots System. Victoria, BC, Canada, 1998: 1230-1235.
[6] Liu Y, Zhu S Q, Jin B, et al.Sensory navigation of autonomous cleaning robots[C]// Hangzhou: 5th World Conference on Intelligent Control Automation, 2004: 4793-4796.
[7] De Carvalho R N, Vidal H A, Vieira P, et al. Complete coverage path planning and guidance for cleaning robots[C]// Portugal: IEEE International Symposium on industrial Electrontics, 1997: 677-682.
[8] Ram A, Santamaria J C.Continuous case-based reasoning[J]. Artificial Intelligence (S0004-3702), 1997, 90(1/2): 25-77.
[9] Arleo A, Smeraldi F, Gerstner W.Cognitive navigation based on non-uniform Gabor space sampling, unsupervised growing networks, and reinforcement learning[J]. IEEE Transactions on Neural Network (S1045-9227), 2004, 15(3): 639-652.
[10] 李开生, 张慧慧, 费仁元, 等. 具有遍历特性的移动机器人规划方法的研究[J]. 机器人, 2001, 23(6): 486-492.
Li Kaisheng, Zhang Huihui, Fei Renyuan, et al.Research on Path Planning of Mobile Robot with Covering-path Feature[J]. Robot, 2001, 23(6): 486-492.
[11] 刘松, 李志蜀, 李奇. 机器人全覆盖最优路径规划的改进遗传算法[J]. 计算机工程与应用, 2009, 45(31): 245-248.
Liu Song, Li Zhishu, Li Qi.Improved Genetic Algorithm Optimal Area Covering Path Planning for Family Robot[J]. Computer Engineering and Applications, 2009, 45(31): 245-248.
[12] 邸业飞, 张崎. 基于矩形区域分解重组算法的吸尘路径规划[J]. 现代电子技术, 2011, 34(7): 147-149.
Di Yefei, Zhang Qi.Vacuum Dust Absorption Path Planning Based on Rectangular Domain Decomposition and Restructuring Algorithm[J]. Modern Electronics Technique, 2011, 34(7): 147-149.
[13] Hyun M, Jeon H M, Jeong W Y, et al.Virtual Door-Based Coverage Path Planning for Mobile Robot[C]// IEEE International Symposium on Industrial Electronics. Seoul Korea, 2009: 658-663.
[14] Habib M A, Alan M S, Siddque N H.Optimizing coverage performance of multiple random path-planning robots[J]. Paladyn (S2080-9778), 2012, 3(1): 11-22.
[15] Choset H.Coverage for robotics—a survey of recent results[J]. Annals of Mathematics and Artificial Intelligence (S1012-2443), 2001, 31(1-4): 113-126.
[16] Latombe J C.Robot motion planning[M]. Dordrecht: Kluwer Academic Publishers, 1996.
[17] Choset H.Coverage of known spaces: the boustrophedon cellular decomposition[J]. Autonomous Robots (S0929-5593), 2000, 9(3): 247-253.
[18] Tian C, Liu Y, Feng S, et al.Complete coverage algorithm of mobile robot—rectangular decomposition method[J]. Chinese Journal of Mechanical Engineering (S1000-9345), 2005, 40(10): 56-61.
[19] Garcia E, Gonzalez, Santos P.Mobile-robot navigation with complete coverage of unstructured environments[J]. Robotics and Autonomous Systems (S0921-8890), 2004, 46(4): 195-204.
[20] Meng M, Yang X.A Neural Network Approach to Real-Time Trajectory Generation[C]// In IEEE Proc. Int. Conf. Robot .Autom at .Leuven, Belgium: IEEE, 1998: 1725-1730.
[21] Zhu B, Wang Z M, Liu Z L. A Biological Inspired Neural Network Approach to Robot Path Planning in Unknown Environment[J]. Applied Mechanics and Materials (S1660-9336), 2014, 602-605: 1399-1402.
[22] Yang X, Luo C M.A Neural Network Approach to Complete Coverage Path Planning[J]. Systems, Man, and Cybernetics (S2168-2216), 2004, 34(1): 718-725.
[23] Zhu D Q, Li W C, Yan M Z, et al.The path planning of AUV based on D-S information fusion map building and bio-inspired neural network in unknown dynamic environment[J]. International Journal of Advanced Robotic Systems (S1729-8814), 2014, 11(1): 1-14.
[24] Zhu D Q, Sun B.The bio-inspired neural network based hybrid sliding-mode tracking control for open-frame underwater vehicles[J]. Engineering Applications of Artificial Intelligence (S0952-1976), 2013, 26(10): 2260-2269.
[25] 朱大奇, 孙兵, 李利. 基于生物启发模型的AUV三维自主路径规划与安全避障算法[J]. 控制与决策, 2015, 30(5): 798-806.
Zhu Daqi, Sun Bing, Li Li.Algorithm for AUV’s 3-D Path Planning and Safe Obstacle Avoidance based on Biological Inspired Model[J]. Control and Decision, 2015, 30(5): 798-806.

基于行为策略的AUV全覆盖信度函数路径规划算法

Complete Coverage Belief Function Path Planning Algorithm of Autonomous Underwater VehicleBased on Behavior Strategy

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