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3D gaze cursor: continuous calibration and end-point grasp control of robotic actuators

机译:3D注视光标:机器人执行器的连续校准和终点控制

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摘要

© 2016 IEEE.Eye movements are closely related to motor actions, and hence can be used to infer motor intentions. Additionally, eye movements are in some cases the only means of communication and interaction with the environment for paralysed and impaired patients with severe motor deficiencies. Despite this, eye-tracking technology still has a very limited use as a human-robot control interface and its applicability is highly restricted to 2D simple tasks that operate on screen based interfaces and do not suffice for natural physical interaction with the environment. We propose that decoding the gaze position in 3D space rather than in 2D results into a much richer spatial cursor signal that allows users to perform everyday tasks such as grasping and moving objects via gaze-based robotic teleoperation. Eye tracking in 3D calibration is usually slow - we demonstrate here that by using a full 3D trajectory for system calibration generated by a robotic arm rather than a simple grid of discrete points, gaze calibration in the 3 dimensions can be successfully achieved in short time and with high accuracy. We perform the non-linear regression from eye-image to 3D-end point using Gaussian Process regressors, which allows us to handle uncertainty in end-point estimates gracefully. Our telerobotic system uses a multi-joint robot arm with a gripper and is integrated with our in-house GT3D binocular eye tracker. This prototype system has been evaluated and assessed in a test environment with 7 users, yielding gaze-estimation errors of less than 1cm in the horizontal, vertical and depth dimensions, and less than 2cm in the overall 3D Euclidean space. Users reported intuitive, low-cognitive load, control of the system right from their first trial and were straightaway able to simply look at an object and command through a wink to grasp this object with the robot gripper.
机译:©2016 IEEE。眼睛运动与运动动作密切相关,因此可用于推断运动意图。另外,在某些情况下,眼球运动是严重运动障碍的瘫痪和受损患者与环境交流和互动的唯一手段。尽管如此,眼动追踪技术作为人机控制界面的使用仍然非常有限,其适用性高度受限于在基于屏幕的界面上运行的2D简单任务,并且不足以与环境进行自然的物理交互。我们建议将注视位置在3D空间而不是2D中进行解码会导致空间光标信号更加丰富,从而使用户能够执行日常任务,例如通过基于注视的机器人遥操作来抓取和移动物体。 3D校准中的眼睛跟踪通常很慢-我们在这里证明,通过使用完整的3D轨迹进行机械臂生成的系统校准,而不是简单的离散点网格,可以在短时间内成功完成3维注视校准,并且精度高。我们使用高斯过程回归器执行从眼图到3D端点的非线性回归,这使我们能够优雅地处理端点估计中的不确定性。我们的遥控机器人系统使用带有抓爪的多关节机械臂,并与我们内部的GT3D双眼眼动仪集成。该原型系统已经在具有7位用户的测试环境中进行了评估和评估,在水平,垂直和深度维度上的凝视估计误差小于1cm,而在整个3D欧几里德空间中的凝视估计误差小于2cm。用户从第一次试用就报告了直观,低认知的负载,对系统的控制,并且能够直接观察物体并通过眨眼命令以使用机器人抓取器抓住该物体。

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