不确定海洋环境下AUV分层任务规划与重规划研究

南京大学学报(自然科学版) ›› 2015, Vol. 51 ›› Issue (1): 148–156.

不确定海洋环境下AUV分层任务规划与重规划研究

张汝波1,2,童海波1,史长亭1,刘海涛1

出版日期:2015-01-04 发布日期:2015-01-04
作者简介:(1．哈尔滨工程大学,哈尔滨,150001;2．大连民族学院,大连,116600)
基金资助:
国家自然科学基金(60975071，61100005),中央高校基本科研业务费专项资金(HEUCF061004)

Research on autonomous underwater vehicle hierarchical mission planning and re-planning in uncertain environment

Zhang Rubo1, 2*, Tong Haibo1, Shi Changting1, Liu Haitao1

Online:2015-01-04 Published:2015-01-04
About author:(1. Harbin Engineering University College Computer Science and Technology, Harbin, 150001, China;
2. Dalian Nationalities University College of Electromechanical&Information Engineering, Dalian, 116600, China)

摘要/Abstract

摘要： 自主水下机器人（Autonomous Underwater Vehicle，AUV）是海洋开发与探索的有效工具。为提高AUV在复杂海洋环境、任务多变以及通信受限等不确定条件下的自适应性和任务执行的可靠性，首先，研究并设计了基于分层思想的AUV任务规划与重规划体系结构；其次，针对不同的层次分别提出了基于与或分解树的使命规划、基于有限状态机的任务规划；然后，阐述了分层重规划的意义并设计了分层重规划监督决策的具体算法；最后，仿真实验表明了所设计的分层体系结构及分层任务规划与重规划监督决策算法，能显著提高AUV不确定条件下的自适应性和自主完成任务可靠性。

Abstract: Autonomous underwater vehicle is effective equipment for ocean development and exploration. It is important to improve the adaptability and reliability of AUV mission execution in consideration of various uncertainties such as complex ocean environment, changeable mission and limited communication. In this paper, we study and design a hierarchical mission planning and re-planning architecture. It is based on hierarchical task network which can describe experiential knowledge and solve problems efficiently. Secondly, we propose the mission planning method based on and-or decomposition tree and task planning scheme based on the finite state machine respectively. These methods achieve the hierarchical re-planning model and handle different uncertainty on different level. Then, the significance of hierarchical mission re-planning is expounded and the supervision and decision-making algorithm for hierarchical mission re-planning is presented. Finally, simulation experiments indicate that the designed hierarchical architecture, mission planning and re-planning supervision and decision-making methods can significantly improve the adaptability and reliability of AUV mission execution.

张汝波1,2,童海波1,史长亭1,刘海涛1. 不确定海洋环境下AUV分层任务规划与重规划研究[J]. 南京大学学报(自然科学版), 2015, 51(1): 148–156.

Zhang Rubo1, 2*, Tong Haibo1, Shi Changting1, Liu Haitao1. Research on autonomous underwater vehicle hierarchical mission planning and re-planning in uncertain environment

[J]. Journal of Nanjing University(Natural Sciences), 2015, 51(1): 148–156.

参考文献

[1] 徐玉如, 肖坤. 智能海洋机器人技术进展. 自动化学报, 2007, 33(5): 518~521.
[2] Zhang Y W, Robert S, John P, et al. Design and tests of an adaptive triggering method for capturing peak samples in a thin phytoplankton layer by an AUV. IEEE Journal of Oceanic Engineering, 2010, 35(4):785~796.
[3] Gabriele F, Michael V, Dana R. A novel trigger-based method for hydrothermal vents prospecting using an autonomous underwater robot. Autonomous Robot, 2010, 29(1):67~83.
[4] 刘林峰, 刘业. 基于满Steiner树问题的水下无线传感器网络拓扑愈合算法研究. 通信学报, 2010, 31(9): 30~37.
[5] 郭忠文, 罗汉江, 洪锋等. 水下无线传感器网络的研究进展. 计算机研究与发展, 2010, 47(3): 377~389.
[6] 赵涛, 刘明雍, 周良荣. 自主水下航行器的研究现状与挑战. 火力与指挥控制, 2010, 35(6): 1~6.
[7] Eickstedt D P, Scott R. The backseat control architecture for autonomous robotic vehicles: A case study with the Iver2 AUV. OCEANS 2009, MTS/IEEE Biloxi - Marine Technology for Our Future: Global and Local Challenges, 2009: 1~8.
[8] Zhang L, Jiang D P, Zhao J X. The basic control system of an ocean exploration AUV. Applied Mechanics and Materials, 2013, 411-414:1757~1761.
[9] Conor M G, Frederic P, Kanna R, et al. A deliberative architecture for AUV control. In: Proceedings of 2008 IEEE International Conference on Robotics and Automation. New York: IEEE Press, 2008: 1049~1054.
[10] 张汝波, 顾国昌, 杨歌等. 具有学习能力的智能机器人体系结构研究. 华中科技大学学报（自然科学版）, 2004, 32(z): 58~60.
[11] 李彩虹, 李贻斌, 宋勇. 一种移动机器人混合式体系结构. 中南大学学报（自然科学版）, 2007, 38(z1):445~449.
[12] 贺倩, 冯志勇. 基于人工智能技术的认知无线网络重构决策算法. 通信学报, 2012, 33(7): 96~102.
[13] Zhang R, Tong H, Shi C. A control architecture for mission re-planning and plan repair of AUV. Applied Mechanics and Materials, 2014, 462-463:794~797.
[14] 张汝波, 尹莉莉, 顾恒文. 不确定海洋环境下AUV环境感知方法研究. 计算机研究与发展, 2013, 50(9):1981~1991.
[15] 赵晶晶, 刘大有, 蔡敦波. 一致性规划中互斥量合成方法研究. 计算机研究与发展, 2012, 49(6): 1279~1287.
[16] 邵杰, 杨静宇, 万鸣华等. 基于学习分类的多机器人路径规划收敛性研究. 计算机研究与发展, 2010, 47(5): 948~955.
[17] 唐攀, 王红卫, 王哲. 基于预案模板的HTN规划知识建模方法及其应用. 计算机科学, 2010, 37(10): 202~206.
[18] 徐秀. 多层子树堆排序任务匹配调度算法. 通信学报, 2010, 31(8): 81~86.
[19] Brad R, Marc R, John T, et al. Applications of multi-objective evolutionary algorithms to air operations mission planning. In: Proceedings of the 10th Annual Conference Companion on Genetic and Evolutionary Computation. New York: ACM, 2008: 1879~1884.
[20] Pedro P, David M L. Adaptive mission planning: The embedded OODA loop. In: Proceedings of the 3rd SEAS DTC Technical Conference. New York: IEEE/ACM, 2008: 1~9.
[21] Jnaneshwar D, Kanna R, Sergey F, et al. Towards marine bloom trajectory prediction for AUV mission planning. In: Proceedings of 2010 IEEE International Conference on Robotics and Automation. New York: IEEE Press, 2010: 4784~4790.
[22] 余杨, 闵富红, 倪晓武等. 猎人机器人通信跟瞄理论和系统研究. 通信学报, 2011, 32(1): 52~58.
[23] 陶通, 黄亚楼, 苑晶等. 基于协助校正方法的多机器人主动同时定位与建图. 模式识别与人工智能, 2012, 25(3): 534~542.
[24] Kanakakis V, Tsourveloudis N. Evolutionary path planning and navigation of autonomous underwater vehicles. In: Proceedings of 2007 Mediterranean Conference on Control and Automation. New York: IEEE Press, 2007: 1~6.
[25] Pedro P, Emilio M, Yvan R, et al. Fault tolerant adaptive mission planning with semantic knowledge representation for autonomous underwater vehicles. In: Proceedings of 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. New York: IEEE Press, 2008: 2593~2598.
[26] Nuno A, Anibal M, Patricia R, et al. Automatic interface for AUV mission planning and supervision. Oceans 2010, 2010: 1~5.
[27] Eichhorn M, Willianms C, Bachmayer R, et al. A mission planning system for the AUV “SLOCUM Glider” for the Newfoundland and Labrador Shelf. Oceans 2010, 2010: 1~9.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed