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题名: 自主水下机器人深海热液羽流追踪研究
其他题名: Deep-Sea Hydrothermal Plume Tracing with Autonomous Underwater Vehicles
作者: 田宇
导师: 张艾群
分类号: TP242
关键词: 自主水下机器人 ; 热液喷口定位 ; 热液羽流探测 ; 羽流追踪 ; 基于行为规划
索取号: TP242/T58/2012
学位专业: 机械电子工程
学位类别: 博士
答辩日期: 2012-05-25
授予单位: 中国科学院沈阳自动化研究所
学位授予地点: 中国科学院沈阳自动化研究所
作者部门: 水下机器人技术研究室
中文摘要: 由于其潜在的巨大经济价值和科学研究价值,海底热液喷口调查研究已经成为近年来国际上深海调查研究工作的一个重点和热点。鉴于自主水下机器人(Autonomous Underwater Vehicle,AUV)的功能特点,应用AUV自主寻找、精确定位、和探测热液喷口已经成为将来海底热液喷口调查的必然趋势。为了提高基于AUV的热液喷口寻找、精确定位和探测的作业效率,赋予AUV基于热液羽流探测传感器信息在线决策、规划,自主追踪热液羽流进而快速找到并精确定位热液喷口的能力,是需要研究和解决的一个关键问题。 本论文的研究结合机器人学国家重点实验室自主课题“基于行为的AUV深海热液羽流追踪研究(2009-Z03)”,国家自然科学基金面上项目“自主水下机器人深海热液羽流追踪研究(61075085)”、和青年基金项目“自主水下机器人海底热液喷口自主探测研究(41106085)”,并针对我国863高技术研究发展计划正在支持研制的用于海底热液活动调查AUV的将来应用需求,重点对AUV基于仿生行为追踪深海热液羽流的策略、和AUV实现相应策略的基于行为的在线规划算法开展应用基础研究工作,研究和解决其涉及的关键问题,为研究和设计高效的AUV寻找、定位、和探测热液喷口的作业方案和与之相应的AUV在线任务规划算法提供必要的理论基础和技术储备。本论文的主要研究内容及成果如下:(1)仿真实验环境:由于进行真实海洋环境中的热液羽流追踪实验复杂度和成本均较高,因此本文主要基于计算机仿真环境开展研究工作。由于现有的自主机器人追踪湍羽流仿真环境无法满足研究需要,特别是现有仿真环境中的羽流和流场不能体现热液羽流追踪问题的复杂性因素;因此,针对研究需要,基于湍流仿真的拉格朗日粒子随机行走方法、和所研究的热液羽流追踪问题复杂性因素分析,提出一种热液羽流仿真模型,基于该模型的仿真流场和羽流充分体现了本文研究所需要的复杂性因素,包括流场非均匀和非定常,羽流分布不规则、不连续、空间尺度大,羽流轴线弯曲,以及羽流含有浮力上升部分和包含非守恒示踪物质。基于该仿真模型,设计、实现了一个模块化体系结构的计算机仿真研究环境。研发的仿真环境是开展AUV热液羽流追踪研究工作的关键前提。(2)基于行为的任务规划器:本文采用基于行为的规划方法研究和设计AUV追踪热液羽流的在线规划算法。针对研究需要,借鉴自主机器人离散事件动态系统监督控制研究,提出一种模块化的基于行为的任务规划器体系结构,包括行为协调模块、行为模块和制导模块。其中制导模块为AUV追踪热液羽流的自主行为中所需调用的制导函数的集合。重点研究了其中的路径跟踪制导,针对欠驱动AUV,提出一种三维路径跟踪制导算法。此外,为了使基于PID控制的AUV能够在复杂海洋环境的影响下精确实现规划的羽流追踪路径,提出了一种混合模糊P+ID控制方法,在线性PID控制中引入非线性以提高其性能。为了对提出的体系结构和设计的制导函数进行验证,基于该体系结构和制导函数,设计了AUV在二维水平面以自适应梳形策略探测非浮力羽流的基于行为规划算法,并利用研究室的一台小型AUV和该规划算法在我国大连湾海域进行了羽流探测实验,实验结果验证了以上的研究内容。本文后续研究和设计的AUV追踪热液羽流的基于行为规划算法即基于该体系结构和其中的制导模块,因此,本部分的研究和实验,是研究和设计AUV追踪热液羽流的基于行为规划算法的重要基础。(3)热液非浮力羽流追踪:研究并解决AUV热液非浮力羽流追踪问题:AUV在固定深度的二维水平面寻找热液非浮力羽流、追踪热液非浮力羽流至其上游源头并对其源头位置进行估计。模仿生物追踪羽流采用的逆流向上的“之”字形追踪策略,提出一种AUV热液非浮力羽流追踪策略;并针对该追踪任务和追踪策略,抽象出十个AUV自主行为。结合AUV的动力学,探测传感器的噪声,热液羽流的尺度、弯曲和流场的变化等特性,和作业任务的目标等,基于研究的体系结构和制导函数,设计了实现各行为的算法,并对其关键参数的设计、选取依据和原则进行了深入细致分析。最后,通过仿真实验环境、和近岸海洋环境罗丹明羽流追踪实验,对追踪策略和基于行为的规划算法进行研究和验证。理论分析、仿真、和实验结果表明,设计的策略和算法,能够实现AUV在复杂海洋环境中对非浮力湍羽流的有效追踪和其源头的定位。(4)热液浮力羽流追踪和喷口定位:研究并解决AUV追踪热液浮力羽流和定位热液喷口问题:AUV在三维空间中追踪热液浮力羽流到达海底热液喷口位置并实现对热液喷口的精确定位。提出两种热液浮力羽流追踪策略,分别为“之”字形下降追踪策略和垂直下降追踪策略,设计了实现相应策略的基于行为的规划算法,并通过仿真环境对策略和算法进行了研究和验证。针对定位多个临近热液喷口问题,研究了基于占用栅格标图算法的热液喷口位置估计,并提出了一种AUV基于羽流信息精确定位热液喷口的算法。最后,在本部分研究的基础上,提出一种AUV自主探测热液浮力羽流以高效采集其时空分布数据的策略和算法,进而提高基于AUV的热液浮力羽流测绘作业效率。
英文摘要: Due to their great economical and scientific value, sea-floor hydrothermal vents have become the focus of many current international deep-sea exploration expeditions. Considering the capabilities and merits of autonomous underwater vehicles (AUVs), employing AUVs to search, localize and explore the hydrothermal vents in these expeditions has become the future trend. In order to further improve the AUV based mission efficiency of hydrothermal vent searching, localization, and exploration, a key issue is to develop on-line planning algorithms that could navigate an AUV in response to real-time sensor data to find the hydrothermal plumes within an identified hydrothermal area, to trace the hydrothermal plumes to their sources, and finally to reliably identify the vents’ location on the seafloor (in this thesis, this problem will be referred to as hydrothermal plume tracing (HPT)).     Based on the research topics of the program of the State Key Laboratory of Robotics “Behavior-based Deep-sea Hydrothermal Plume Tracing with Autonomous Underwater Vehicles (Grant No. 2009-Z03)”, the National Natural Science Foundation “Deep-Sea Hydrothermal Plume Tracing with Autonomous Underwater Vehicles (Grant No. 61075085)”, and the National Natural Science Foundation “Autonomous Survey of Sea-Floor Hydrothermal Vents with Autonomous Underwater Vehicles (Grant No. 41106085)”, the topic of this thesis is to develop bio-inspired strategies and corresponding behavior-based planning (BBP) algorithms for HPT, and investigate the related key theoretical and technical issues. The efforts of this thesis will provide theoretical foundation and technical reserve for developing efficient mission strategies and corresponding AUVs’ on-line planning algorithms for hydrothermal vent searching, localization, and exploration. The thesis’s main research contents and results are as follows.   Chapter 2 presents a hydrothermal plume simulation model, and a computer simulation environment for HPT research. Due to the high costs and complexities of field experiments, we primarily employ computer simulation to investigate HPT strategies and BBP algorithms. Because the existed simulation environments for plume-tracing research can not meet our requirements, especially in that the simulated plumes do not reflect the features of hydrothermal plumes, this chapter proposes a hydrothermal plume simulation model. The model is developed based on the Lagrangian particle random walk algorithm for turbulent plume simulation and analysis of the complicating factors of the HPT problem. Simulated plumes via this model could capture the key features of deep-sea hydrothermal plumes which complicate the HPT problem, such as the flow being non-steady and non-uniform, the plume distribution being irregular and intermittency, the plume centerline being meandrous, and the plume containing large-scale buoyant stem and non-conservative tracer. Based on the simulation model, a computer simulation environment was designed and developed using modular architecture. The proposed model and the developed simulation environment are key prerequisites for the following HPT research in this thesis.    In this thesis, we employ the BBP method to develop AUVs’ on-line mission planning algorithms for HPT. Chapter 3 describes the employed architecture to coordinate the behaviors, and the designed guidance functions that are to be used by the AUV HPT behaviors. Based on the study of discrete event supervisory control for autonomous vehicles, this chapter proposes a modular behavior-based planner architecture, which is composed of three chained modules: supervisor module, behavior module, and guidance module. The supervisor module is responsible for behavior coordination, and the guidance module is a set of designed guidance functions to be called by the behavior module. The three-dimensional path-following guidance is mainly studied, and a guidance algorithm for underactuated AUVs to follow predefined three-dimensional path is proposed. In addition, to improve the PID-based motion control performance of AUVs, this chapter proposes a new hybrid fuzzy P plus ID control algorithm. In order to verify the proposed architecture and designed guidance functions, a BBP algorithm that realizes a proposed adaptive lawn-mower strategy to map a non-buoyant plume is developed. The developed behavior-based planner was implemented on an AUV to perform in-water tests, which were conducted in Dalian Bay, China. The successful field experiments demonstrate that the planner effectively navigates the AUV to map a rhodamine plume developed in near-shore ocean environment. The planning algorithm, and the experimental setup and results are presented in the last of this chapter.      Chapter 4 investigates the issue of hydrothermal non-buoyant plume tracing: the AUV first searches for the non-buoyant plume in a predefined operation area, then traces the plume to its up-flow source and finally estimate the source location. In this mission, the AUV operates in a two-dimensional horizontal plane. Inspired by the zigzag plume-tracing strategy employed by animals, a hydrothermal non-buoyant plume tracing strategy is proposed. Based on the plume-tracing mission profile and the strategy, ten AUV behaviors are abstracted. Algorithms for each behavior are designed and developed based on the architecture and guidance functions described in Chapter 2, and are presented in detail. In addition, the design and selection of the key parameters related to the behaviors and their effects on the mission performance are analyzed and discussed. At last, the tracing strategy and the BBP algorithm are verified via computer simulation, and rhodamine plume tracing experiments conducted in near-shore ocean environment. The theoretical analysis, simulation and filed experiments demonstrate that the designed strategy and the BBP algorithm could enable the AUV to effectively trace non-buoyant turbulent plume to its source and accurately estimate the source location in complex ocean environment.       Chapter 5 focuses on the issue of hydrothermal buoyant plume tracing and vent localization: the AUV traces the buoyant plume in three-dimensions from the top-end of the buoyant stem to the seafloor and finally localizes the hydrothermal vent. In this chapter, we propose two buoyant plume tracing strategies, one is referred to as zigzag down strategy and the other is referred to as vertical down strategy. BBP algorithms are designed and developed to realize these two strategies respectively. And the effectiveness of these two BBP algorithms is verified via computer simulation. In addition, this chapter investigates the approach to localizing multiple nearby hydrothermal vents, and an occupancy grid mapping algorithm that could be employed to estimate multiple vents is presented, and a method to accurately estimate a vent location is proposed. At last, based on above research results, a strategy for an AUV to adaptively map a hydrothermal buoyant plume is proposed, and verified via computer simulation.
语种: 中文
产权排序: 1
内容类型: 学位论文
URI标识: http://ir.sia.cn/handle/173321/9292
Appears in Collections:水下机器人研究室_学位论文

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Recommended Citation:
田宇.自主水下机器人深海热液羽流追踪研究.[博士学位论文 ].中国科学院沈阳自动化研究所 .2012
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