利用骨骼位置估算遥控机械手操作员的人为错误率

Thomas Piercy, Guido Herrmann, Angelo Cangelosi, Ioannis Dimitrios Zoulias, Erwin Lopez
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引用次数: 0

摘要

在当前的远程机器人和远程机械手应用中,操作员必须执行各种各样的任务,而这些任务往往具有很高的失败风险。利用观测到的操作员特征生成基于数据的行为估计的系统,可用于降低工业远程操作中的风险。本文介绍了一种非侵入式远程操作员生物机械特征捕捉方法,用于试用新型人为错误率估算器,在未来的工作中,该估算器将通过向操作员提供行为和姿势反馈来提高操作安全性。操作员监控研究是在英国原子能机构 RACE 使用 MASCOT 远程操作系统在现场进行的;在观察过程中,操作员要完成受控任务。在现有的车辆驾驶员意图估计和机器人手术操作员分析工作的基础上,我们使用市售的深度摄像头采集三维点云数据,以估计操作员的骨骼姿势。我们对总共 14 名操作员进行了长达约 8 小时的观察和记录,每个人都完成了一项基线任务和一项旨在诱发可检测但安全的碰撞的任务。我们对骨骼姿势进行了估计,记录了碰撞统计数据,并进行了基于问卷的心理评估,从而提供了一个定性和定量数据的数据库。然后,我们尝试使用统计和机器学习回归技术(SVR)进行数据驱动分析,以估算碰撞率。我们还进一步对所选特征进行了输入变量敏感性分析,并将分析结果提交给大家。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using skeletal position to estimate human error rates in telemanipulator operators
In current telerobotics and telemanipulator applications, operators must perform a wide variety of tasks, often with a high risk associated with failure. A system designed to generate data-based behavioural estimations using observed operator features could be used to reduce risks in industrial teleoperation. This paper describes a non-invasive bio-mechanical feature capture method for teleoperators used to trial novel human-error rate estimators which, in future work, are intended to improve operational safety by providing behavioural and postural feedback to the operator. Operator monitoring studies were conducted in situ using the MASCOT teleoperation system at UKAEA RACE; the operators were given controlled tasks to complete during observation. Building upon existing works for vehicle-driver intention estimation and robotic surgery operator analysis, we used 3D point-cloud data capture using a commercially available depth camera to estimate an operator’s skeletal pose. A total of 14 operators were observed and recorded for a total of approximately 8 h, each completing a baseline task and a task designed to induce detectable but safe collisions. Skeletal pose was estimated, collision statistics were recorded, and questionnaire-based psychological assessments were made, providing a database of qualitative and quantitative data. We then trialled data-driven analysis by using statistical and machine learning regression techniques (SVR) to estimate collision rates. We further perform and present an input variable sensitivity analysis for our selected features.
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