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Effects of Throughput Delay on Perception of Robot Teleoperation and Head Control Precision in Remote Monitoring Tasks

机译:吞吐量延迟对远程监视任务中机器人遥距感知和头部控制精度的影响

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摘要

For robot teleoperators, replicating head motion in a remote environment is often necessary to complete monitoring and face-to-face service tasks. Especially in the case of a stereoscopic camera rig, remote cameras need to be moved to match the operator's head position and optical axes in order to interact with a remote entity, follow targets, or reorient. However, mechanical, computational, and network delay in such teleoperation can cause intersensory conflict, perceptual deficits, and reduction in performance, especially during activities that require head rotation. In this article, we evaluate the effects of view reconstruction on performance and perception for remote monitoring tasks. To do so, we first implemented a panoramic reconstruction method to reduce perceived latency in a humanoid robot, which allows us to compare and contrast latency. Next, we designed a bidirectional remote control system for the robot and set up a series of experiments where participants had to conduct focused head control tasks through the perspective of the robot. This allowed us to compare the effects of latency on head movement, accuracy, and subjective perception of the interface and remote teleoperation. Results showed that panoramic reconstruction significantly improved perception and comfort during teleoperation, but that performance only improved for tasks requiring slower head movements.
机译:对于机器人远程操作员,通常需要在远程环境中复制头部动作以完成监视和面对面服务任务。尤其是在使用立体摄像机的情况下,需要移动远程摄像机以匹配操作员的头部位置和光轴,以便与远程实体进行交互,跟随目标或重新定向。但是,在这种遥距操作中,机械,计算和网络延迟会导致感官冲突,感知缺陷和性能下降,尤其是在需要旋转头部的活动中。在本文中,我们评估了视图重建对远程监视任务的性能和感知的影响。为此,我们首先实现了全景重建方法,以减少人形机器人的感知等待时间,这使我们可以比较和对比等待时间。接下来,我们为机器人设计了双向远程控制系统,并进行了一系列实验,参与者必须从机器人的角度执行集中的头部控制任务。这使我们能够比较延迟对头部运动,准确性以及对界面和远程遥控操作的主观感知的影响。结果表明,全景重建可显着改善遥操作过程中的感知力和舒适度,但性能仅在需要缓慢移动头部的任务时得到改善。

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  • 来源
    《Presence》 |2020年第2期|226-241|共16页
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    Cybermedia Center Osaka University 1-32 Machikaneyama;

    Center for Information and Neural Networks NICT 1-4 Yamadaoka;

    International Research Center for Neurointelligence The University of Tokyo 7-3-1 Hongo;

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