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基于三个光学鼠标传感器的桌面式移动康复机器人绝对位置计算

Absolute position calculation for a desktop mobile rehabilitation robot based on three optical mouse sensors.

作者信息

Zabaleta Haritz, Valencia David, Perry Joel, Veneman Jan, Keller Thierry

机构信息

Tecnalia Research and Innovation Paseo Mikeletegi 1, 20011 Donostia.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:2069-72. doi: 10.1109/IEMBS.2011.6090383.

Abstract

ArmAssist is a wireless robot for post stroke upper limb rehabilitation. Knowing the position of the arm is essential for any rehabilitation device. In this paper, we describe a method based on an artificial landmark navigation system. The navigation system uses three optical mouse sensors. This enables the building of a cheap but reliable position sensor. Two of the sensors are the data source for odometry calculations, and the third optical mouse sensor takes very low resolution pictures of a custom designed mat. These pictures are processed by an optical symbol recognition algorithm which will estimate the orientation of the robot and recognize the landmarks placed on the mat. The data fusion strategy is described to detect the misclassifications of the landmarks in order to fuse only reliable information. The orientation given by the optical symbol recognition (OSR) algorithm is used to improve significantly the odometry and the recognition of the landmarks is used to reference the odometry to a absolute coordinate system. The system was tested using a 3D motion capture system. With the actual mat configuration, in a field of motion of 710 × 450 mm, the maximum error in position estimation was 49.61 mm with an average error of 36.70 ± 22.50 mm. The average test duration was 36.5 seconds and the average path length was 4173 mm.

摘要

ArmAssist是一款用于中风后上肢康复的无线机器人。了解手臂的位置对于任何康复设备来说都至关重要。在本文中,我们描述了一种基于人工地标导航系统的方法。该导航系统使用三个光学鼠标传感器。这使得能够构建一个廉价但可靠的位置传感器。其中两个传感器是里程计计算的数据源,第三个光学鼠标传感器对定制设计的垫子拍摄低分辨率图片。这些图片由光学符号识别算法进行处理,该算法将估计机器人的方向并识别放置在垫子上的地标。文中描述了数据融合策略,以检测地标的错误分类,从而仅融合可靠信息。光学符号识别(OSR)算法给出的方向用于显著改进里程计,地标识别用于将里程计参考到绝对坐标系。该系统使用三维运动捕捉系统进行了测试。在实际的垫子配置下,在710×450毫米的运动区域中,位置估计的最大误差为49.61毫米,平均误差为36.70±22.50毫米。平均测试持续时间为36.5秒,平均路径长度为4173毫米。

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