Advanced Environment Modelling for Remote Teleoperation to Improve Operators’ Experience

Yixiang Jin, Daniel Alonso Paredes Soto, John Anthony Rossiter, Sandor M Veres

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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This work presents a novel intelligent robot perception system, including a real-time, high-quality, 3D scanning pipeline for texture-less scenes and a human-supervised grasping system. Comparison is carried out with the state of the art 3D reconstruction systems, and the performance of the proposed system is demonstrated. The scanning methods are applied to a new user interface with object 6D-pose estimation. This work supports human-robot interaction in remote handling operations in hazardous environments by providing a high-quality telepresence. Current teleoperation systems primarily utilise 2D images or point clouds to display the remote workspace to the operator. Operators require extensive training to be able to perceive the spatial relationship between the robot and the target objects by remotely looking at multiple 2D images. Therefore, this paper proposes a new teleoperation system that exploits artificial intelligence to improve the efficiency of operators. The experiments show that the proposed method surpasses state-of-the-art reconstruction systems and successfully complements a simulated nuclear waste handling experiment.
Original languageEnglish
Title of host publicationicARTi '21: Proceedings of the International Conference on Artificial Intelligence and its Applications
PublisherAssociation for Computing Machinery (ACM)
Publication statusPublished - 9 Dec 2021
Externally publishedYes

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