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基于传感器的仿生机器鱼智能控制研究
其他题名Research on Intelligent Control of Biomimetic Robot Fish Based on Sensors
桑海泉
学位类型工学博士
导师谭民 ; 王硕
2005-04-01
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业控制理论与控制工程
关键词仿生机器鱼 智能控制 基本运动 基于行为 视觉导航 Biomimetic Robot Fish Intelligent Control Basic Motion Behavior-based Visual Navigation
摘要在国家高技术研究发展计划(863计划)和国家自然科学基金的支持下,本文开展了基于传感器的仿生机器鱼智能控制及协调研究,提出了基于红外传感器的自主避障算法、基于实时视觉信息处理的机器鱼导航控制算法及多机器鱼巡逻控制算法,最后,将控制算法集成到基于仿生机器鱼的水下监测系统上。第一,本文介绍了鱼类游动的特点和仿生机器鱼研究的目的、意义,综述了国内外在鱼类推进机理、仿生机器鱼研制以及控制方面的研究概况、主要研究内容及发展方向,并对本文的选题背景和主要内容进行了介绍。 第二,基于红外传感器阵列,本文提出了自主避障仿生机器鱼设计方案,介绍了机器鱼的设计步骤;对机器鱼的五种上浮下潜设计方案进行了比较,采用附加胸鳍方式实现机器鱼的上浮下潜控制;基于机器鱼的速度控制、方向控制和上浮下潜控制,设计了机器鱼的9种基本运动,提出了一种基于规则推理的智能避障控制方法,并通过避障实验验证了控制方法的有效性。 第三,以实时视觉信息处理为基础,研究了机器鱼视觉导航控制问题。以机器鱼趋光行为为背景,提出了非结构化环境下的机器鱼视觉导航控制策略和控制算法;以管道探测为应用背景,研究基于颜色路标的导航问题,提出了半结构化环境下机器鱼视觉导航控制算法和基于拓扑的管道地图构建方法。机器鱼趋光实验和管道探测实验验证了控制策略和控制算法的有效性。 第四,结合机器鱼巡逻任务,研究了多机器鱼的协调控制问题,设计了机器鱼的基本行为,提出了基于行为的多机器鱼系统控制结构;提出了机器鱼的巡逻控制算法和多机器鱼队形保持算法;最后实验与仿真结果表明了算法的有效性。第五,提出了基于仿生机器鱼的水下监测系统体系结构,介绍了各部分的功能,提出了MEMS传感网络设计及三自由度胸鳍控制方法;定义了机器鱼巡逻方式和巡逻轨迹规划方法,改进了部分基本运动的实现方式及运动控制方式;基于水下监测任务,引入了四种行为,提出了机器鱼的两种控制模式;最后,给出了机器鱼自主游动模式下的多传感器信息融合框架。 最后,对所开展的工作进行了总结,并指出了需要进一步研究的工作。
其他摘要Research on biomimetic robot fish has become one of hotspots in biomimetic robotic field, and it inspires some new ideas for the development of underwater vehicle with high performance. Funded by the National High Technology Program and National Nature Fund Project, research on intelligent control and coordination of biomimetic robot fish based on sensors has been carried out. It presents autonomous an obstacle avoidance control algorithm based on multiple infrared sensors, the navigation control algorithm with real-time visual information processing and the multiple robot fishes patrol control algorithm. Finally, all algorithms are all integrated into underwater monitoring system based on robot fish.Firstly, the characteristics of fish swimming and the research purpose of biomimetic robot fishes are introduced. The research development and main research direction of both fish’s propulsive mechanism and robot fish’s control at home and abroad are reviewed. The background and structure of this thesis are also addressed.Secondly, based on infrared sensor array, the design project of biomimetic robot fish with the ability of autonomous obstacle avoidance is presented, and the design approach of robot fish is introduced. Subsequently, five feasible up-down mechanism projects are compared, and considering the inside structure characteristics of our robot fish, the project of adding a pair of pectoral fins is adopted to realize the up-down motion of robot fish. On the basis of speed control, swimming orientation control and up-down control, nine basic motions are designed, and an intelligent control approach based rule reasoning. The obstacle avoidance experiments are carried out and the experimental results have shown the validity of this control method.Thirdly, on the basis of real-time visual information processing, the visual navigation control problem is discussed. With the background of robot fish phototropism behavior, visual navigation control policy and algorithm is presented in non-structured environment; Based on pipe detecting, the visual navigation control algorithm and pipe map constructing based on topology in semi-structured environment. Finally, robot fish phototropism experiment and pipe detecting experiment have shown the validity of the control policies and algorithms.Fourthly, with the background of robot fish patrol task, the problem of multiple robot fishes is discussed. The basic behaviors are designed and the control framework of behavior-based multiple robot fishes system is put forward. The definitions of patrol modes and patrol trajectory planning approach are depicted. The patrol control algorithms and the formation keeping algorithm of robot fish are presented. The experimental results have shown the validity of the algorithms.
馆藏号XWLW905
其他标识符200218014603172
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5836
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
桑海泉. 基于传感器的仿生机器鱼智能控制研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2005.
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