Self-Repair and Self-Extension by Tightening Screws based on Precise Calculation of Screw Pose of Self-Body with CAD Data and Graph Search with Regrasping a Driver

Takayuki Murooka, K. Okada, M. Inaba
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引用次数: 6

Abstract

In this paper, we propose methods for tightening screws of self-body using a driver, which enable self-repair and self-extension. There are two difficulties for tightening screws of self-body. First, the precise calculation of the screw pose is needed. When calculation with visual images using a camera, the observation error is so high. The merit of the robot is that the robot has CAD data of self-body. There we calculate the precise screw pose with self CAD data. Second, because of the small closed links when tightening screws of self-body, that the robot cannot move the driver for rotating around the screw sometimes happens because inverse kinematics cannot be solved. To solve this problem, we propose a method of tightening motion generation with regrasping a driver if inverse kinematics cannot be solved. With these methods, humanoid robots PR2 and HIRO realized self-repair and self-extension by tightening screws of self-body.
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基于CAD数据精确计算机体螺杆位姿的拧紧螺钉自修自伸及重抓驱动器图搜索
本文提出了一种用驱动器拧紧自体螺钉的方法,该方法可以实现自修复和自延伸。本体螺丝的拧紧有两个难点。首先,需要对螺杆位姿进行精确计算。在使用相机进行视觉图像计算时,观测误差很大。该机器人的优点是具有自身的CAD数据。在此基础上,利用自身的CAD数据计算出精密的螺杆位姿。其次,由于拧紧本体螺丝时的闭合环节较小,有时会由于无法求解逆运动学而导致机器人无法移动驱动器绕螺丝旋转。为了解决这一问题,我们提出了一种不能求解逆运动学的重新抓取驱动器的紧缩运动生成方法。利用这些方法,仿人机器人PR2和HIRO通过拧紧自身的螺丝实现了自我修复和自我延伸。
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