Intuitive robot teleoperation for civil engineering operations with virtual reality and deep learning scene reconstruction. (October 2020)

• Record Type: Journal Article
• Authors: Zhou, Tianyu; Zhu, Qi; Du, Jing
• Is Part Of: Advanced engineering informatics. Volume 46(2020)
• Publication Date: 2020-10
• Subjects: Robot -- Teleoperation -- Virtual reality -- Scene reconstruction -- Deep learning; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2020.101170
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: 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.