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多仿生机器鱼控制与协调研究
其他题名Research on Control and Coordination of Multiple Bio-mimetic Robot Fishes
喻俊志
2003-10-01
学位类型工学博士
中文摘要多仿生机器鱼的控制与协调研究是针对复杂、动态水下环境中多仿生机器 人系统协调作业而开展的相关理论和关键技术的研究,在海洋探测和军事应用 等领域具有重大的应用价值。本文从机器鱼的运动学模型入手,解决运动控制 问题;以实时视觉信息处理技术为前提,为多鱼协调定位提供位姿信息;基于 行为选择机制的协作策略,为机器鱼的任务级协作打下基础;最后,将所有算 法集成到多仿生机器鱼协调实验平台MRFS上。 本文首先对仿生机器鱼和多仿生机器鱼协调的概念、优越性和应用背景进 行了介绍,对国内外在鱼类推进机理和仿生机器鱼控制方面的研究概况及主要 研究内容进行了综述,并对本文的选题背景和主要内容做了介绍。 其次,根据鱼类游动的特点,提取机器鱼的形体参数和运动参数,简化并 改进了鯵科鱼类的运动学模型,并根据实际元器件和实现方法的约束,对机器 鱼进行优化设计;同时,通过机器鱼样机的研制来验证该设计方法的有效性。 第三,将机器鱼的运动控制分为速度控制和方向控制,提出了分段控制的 速度策略以及采用RBF神经网络和PID控制相结合的稳态游速控制方法,采用 模糊控制方法实现了机器鱼的方向控制,结合速度控制和方向控制实现了机器 鱼的点到点控制。同时,采用改进的距离转换方法实现机器鱼的路径规划。 第四,提出了一种基于色度直方图和饱和度直方图的自适应阈值分割算法。 同时,结合计算机并行处理技术,利用汇编级的MMx指令和SSE指令对整个 视觉跟踪算法进行了并行性优化。 第五,引入了六种基本行为,设计了合理的行为选择条件,采用角色分配 ——行为决策——角色转换策略来实现机器鱼间的协调运动。 第六,给出了一种多机器鱼协调系统的软、硬件体系结构,在此结构基础 上提出了多机器鱼协调实验系统的设计方案,开发研制了多仿生机器鱼协调实 验平台,并应用于一种协作型机器鱼的CLB分层式控制结构的研究中。 最后,对本论文所进行的工作和取得的成果加以总结,并指出了需要进一 步研究的工作。
英文摘要The research on control and coordination of multiple bio-mimetic robot fishes is studied for essential theories and key technologies for cooperative task of multiple robots under a complex and dynamic underwater environment, which will provide valuable applications such as oceanic investigation and military mission. In this paper, started with a simplified kinematic model for fish, the robot fish's motion control problem is solved. Proceeded with real-time visual information processing, information of the fishes' position and orientation is obtained for positioning in the following coordinate experiment. By utilizing the behavior-based strategies, coordinate tasks are achieved. Finally, all algorithms are all integrated into Multiple Robot Fishes coordinate System (MRFS). Firstly, the conception, advantages and applications of multiple robot fishes' coordination are introduced. The research development and main research directions 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, according to characteristics of fish's swimming, both body and movement parameters of bio-mimetic robot fish are extracted. After that, a kinematic propulsive model for carangiform propulsion is simplified and improved. Considered the limitations of the electrical and mechanical apparatus, the robot fish design is optimized. In the mean time, a robot fish prototype is developed, which validates the effectiveness of the design method. Thirdly, the fish's motion control task is decomposed into on-line speed control and orientation control. As for the speed control, a piecewise control strategy and a steady-swimming speed control method integrated RBF neural network with PID controller are proposed; as for the orientation control, a fuzzy logic method is employed in the implementation. Combined the speed control with the orientation control, a point-to-point control algorithm is realized. In the meantime, the path planning of the robot fish is achieved by improved Distance Transform method. Fourthly, an adaptive segmentation algorithm based on hue histogram and saturation histogram is presented. At the same time, on the basis of computer parallel processing technology, the overall visual tracking algorithms are optimally parallelized using assembler-based MMX and SSE instructions. Fifthly, six primitive behaviors are imported, and reasonable conditions of behavior selection are designed. The coordinate motions of the fishes are then accomplished by means of motion strategy based on role allocation, behavior selection and role transition. Sixthly, the control architecture of both hardware and software facing multiple robot fishes' coordination is proposed. Based on the above, a design scheme for the multiple robot fishes experimental system is also laid out. The overall physical experimental
关键词仿生机器鱼 协调与控制 运动控制 基于行为 视觉跟踪 Bio-mimetic Robot Fish Coordination And Control Motion Control Behavior-based Visual Tracking
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5786
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
喻俊志. 多仿生机器鱼控制与协调研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2003.
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