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Modeling and Model Learning in Very High Speed Visual Servo Systems

超高速视觉伺服系统中的建模和模型学习

基本信息

批准号：
14205034
负责人：
HASHIMOTO Koichi
金额：
$ 32.03万
依托单位：
Tohoku University (2004)The University of Tokyo (2002-2003)
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (A)
财政年份：
2002
资助国家：
日本
起止时间：
2002 至 2004
项目状态：
已结题

项目摘要

Conventional manipulators are designed to control the end point position with high accuracy based on the joint angle measurement, and are not designed for high-speed control with vision sensors. Therefore, it is difficult to achieve an intellectual, high-speed task for enabling the performance of high-speed vision. And thus the speed-up of the whole visual feedback system highly depends on the speed-up of the manipulation system. We think that introducing high-speed actuator is not enough and it is important to understand the characteristics of the image processing system and the active vision, the hand system and also the design of the control system. In this sense, to achieve the super-high-speed visual servo system, we have to develop the model that describes the dynamics of all elements composing the system ; and the development of the parameter identification technique ; and the model learning algorithm. The following research are done this year :1.Visual servo by high-speed hand system : A high-speed multi finger hand system was developed. An algorithm to catch a falling ball and a falling column stably was proposed, and the performance of the super-high-speed visual servo was evaluated by experiments.2.Microorganism tracking system : A system that can stably observe a swimming paramecium was developed. The position of the pool where a lot of paramecia swim was controlled by a XY stage.3.Expansion of stability area of visual servo : In this research, the method of expanding the stability area was examined in consideration of the restriction with limited camera view.4.Composition and robustness in visual servo system : In this research, it was pointed out that the robustness of the system changes greatly by combining different structures and algorithms.5.Ball capture and lifting task : Ball catching and lifting experiments have been done.

传统的机械手被设计为基于关节角度测量以高精度控制端点位置，并且不被设计用于具有视觉传感器的高速控制。因此，很难实现用于实现高速视觉性能的智能、高速任务。因此，整个视觉反馈系统的加速在很大程度上取决于操作系统的加速。我们认为仅仅引入高速驱动器是不够的，重要的是要了解图像处理系统和主动视觉，手系统的特点，以及控制系统的设计。从这个意义上说，要实现超高速视觉伺服系统，我们必须发展模型，描述组成系统的所有元素的动态;和参数识别技术的发展;和模型学习算法。本论文主要完成了以下几个方面的研究工作：1.基于高速手的视觉伺服：设计了一种高速多指手系统。提出了一种稳定捕捉下落球和下落柱的算法，并通过实验对超高速视觉伺服系统的性能进行了评估。2.微生物跟踪系统：研制了一套能够稳定观测游动草履虫的系统。3.视觉伺服系统稳定区域的扩展：在考虑摄像机视野限制的情况下，研究了扩展稳定区域的方法。4.视觉伺服系统的组成和鲁棒性：在本研究中，指出了不同结构和算法的组合对系统的鲁棒性有很大的影响。5.接球和举球任务：进行了接球和举球实验。