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使用手持示教器通过演示对工业机器人进行基于运动捕捉和增强现实的编程。

Motion capture and AR based programming by demonstration for industrial robots using handheld teaching device.

作者信息

Liu Guoliang, Sun Wenlei, Li Pinwen

机构信息

School of Mechanical Engineering, Xinjiang University, Urumqi, 830017, China.

出版信息

Sci Rep. 2024 Oct 6;14(1):23259. doi: 10.1038/s41598-024-73747-4.

Abstract

In the industrial robots field, efficient and convenient programming methods have been a hot research topic. In recent years, immersive simulation technology has been developing rapidly in many fields, which provides new horizons for the development of industrial robots. This paper presents a HTC VIVE laser scan motion capture and Holohens augmented reality (AR) based interactive Programming by Demonstration (PbD) system for industrial robot. A portable Handheld Teaching Device (HTD) and its calibration algorithm are designed in the system. The portable HTD which is tracked by a laser motion capture system can be viewed as an AR robot end-effector to teach paths. Meanwhile, the AR robot can be simulated in real time during programing. In addition, the robot reproducing the operator's actions at the same position in space is the focus of programming. So, Multi-system registration methods are proposed to determine the relationship between robot systems, motion capture systems and virtual robot systems. Meanwhile, a path planning algorithm is proposed to convert the captured raw path points into robot-executable code. For unskilled operators, they can easily perform complex programming using the HTD. For skilled senior workers, their skills can be quickly learned by robots using the system.

摘要

在工业机器人领域,高效便捷的编程方法一直是热门研究课题。近年来,沉浸式仿真技术在许多领域迅速发展,为工业机器人的发展提供了新视野。本文提出了一种基于HTC VIVE激光扫描运动捕捉和Holohens增强现实(AR)的工业机器人示教编程(PbD)系统。该系统设计了一种便携式手持示教器(HTD)及其校准算法。由激光运动捕捉系统跟踪的便携式HTD可被视为用于示教路径的AR机器人末端执行器。同时,在编程过程中可以实时模拟AR机器人。此外,使机器人在空间中的相同位置重现操作员的动作是编程的重点。因此,提出了多系统配准方法来确定机器人系统、运动捕捉系统和虚拟机器人系统之间的关系。同时,提出了一种路径规划算法,将捕获的原始路径点转换为机器人可执行代码。对于非熟练操作员,他们可以使用HTD轻松执行复杂编程。对于熟练的高级工人,机器人可以使用该系统快速学习他们的技能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6fc8/11456608/60e9edeeb140/41598_2024_73747_Fig1_HTML.jpg

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