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Intuitive robot teleoperation for civil engineering operations with virtual reality and deep learning scene reconstruction

机译:具有虚拟现实和深度学习现场重建的土木工程运营直观的机器人遥通

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摘要

Robotic teleoperation, i.e., manipulating remote robotic systems at a distance, has gained its popularity in various industrial applications, including construction operations. The key to a successful teleoperation robot system is the delicate design of the human-robot interface that helps strengthen the human operator's situational awareness. Traditional human-robot interface for robotic teleoperation is usually based on imagery data (e.g., video streaming), causing the limited field of view (FOV) and increased cognitive burden for processing additional spatial information. As a result, 3D scene reconstruction methods based on point cloud models captured by scanning technologies (e.g., depth camera and LiDAR) have been explored to provide immersive and intuitive feedback to the human operator. Despite the added benefits of applying reconstructed 3D scenes in telerobotic systems, challenges still present. Most 3D reconstruction methods utilize raw point cloud data due to the difficulty of real-time model rendering. The significant size of point cloud data makes the processing and transfer between robots and human operators difficult and slow. In addition, most reconstructed point cloud models do not contain physical properties such as weight and colliders. A more enriched control mechanism based on physics engine simulations is impossible. This paper presents an intelligent robot teleoperation interface that collects, processes, transfers, and reconstructs the immersive scene model of the workspace in Virtual Reality (VR) and enables intuitive robot controls accordingly. The proposed system, Telerobotic Operation based on Auto-reconstructed Remote Scene (TOARS), utilizes a deep learning algorithm to automatically detect objects in the captured scene, along with their physical properties, based on the point cloud data. The processed information is then transferred to the game engine where rendered virtual objects replace the original point cloud models in the VR environment. TOARS is expected to significantly improve the efficiency of 3D scene reconstruction and situational awareness of human operators in robotic teleoperation.
机译:机器人远程操作,即操纵远程机器人系统,在各种工业应用中获得了其普及,包括施工操作。成功的遥操作机器人系统的关键是人机界面的精致设计,有助于加强人类运营商的情境感知。用于机器人遥操作的传统人体机器人界面通常基于图像数据(例如,视频流),导致有限的视野(FOV)和增加用于处理额外空间信息的认知负担。结果,已经探索了基于扫描技术(例如,深度相机和LIDAR)捕获的点云模型的3D场景重建方法,以向人类运营商提供沉浸和直观的反馈。尽管在Telerobotic系统中应用重建的3D场景的额外好处,但仍存在挑战。大多数3D重建方法由于实时模型渲染的难度而利用原始点云数据。重大尺寸的点云数据使得机器人和人类运营商之间的处理和转移困难和缓慢。此外,大多数重建点云模型不包含体重和侵占机等物理性质。基于物理发动机模拟的更丰富的控制机制是不可能的。本文介绍了一个智能机器人遥操作界面,收集,流程,转移,并重建虚拟现实(VR)中工作空间的沉浸式场景模型,并相应地启用直观的机器人控制。所提出的系统,基于自动重建远程场景(TOAR)的Telerobotic操作,利用深度学习算法在捕获的场景中自动检测对象,以及它们的物理属性基于点云数据。然后将处理的信息转移到游戏引擎,其中呈现的虚拟对象替换VR环境中的原始点云模型。预计TOAR将大大提高人工营业术中3D场景重建和情境认识的效率。

著录项

  • 来源
    《Advanced engineering informatics》 |2020年第10期|101170.1-101170.21|共21页
  • 作者

    Tianyu Zhou; Qi Zhu; Jing Du;

  • 作者单位

    Engineering School of Sustainable Infrastructure & Environment University of Florida 1949 Stadium Road 454A Weil Hall Gainesville FL 32611 United States;

    Engineering School of Sustainable Infrastructure & Environment University of Florida 1949 Stadium Road 454A Weil Hall Gainesville FL 32611 United States;

    Engineering School of Sustainable Infrastructure & Environment University of Florida 1949 Stadium Road 460F Weil Hall Gainesville FL 32611 United States;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Robot; Teleoperation; Virtual reality; Scene reconstruction; Deep learning;

    机译:机器人;遥操作;虚拟现实;场景重建;深度学习;

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