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European Journals of Emerging Computer Vision and Natural Language Processing

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Immersive Technologies for Remote Human-Robot Interaction: A Systematic Literature Review

1 Department of Mechano-Informatics, University of Tokyo, Japan
2 School of Computing and Information Systems, University of Melbourne, Australia

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Abstract

The integration of robotic systems into diverse applications necessitates robust and intuitive remote human-robot interaction (RHRI) capabilities. Traditional teleoperation methods often fall short in providing comprehensive situational awareness, immersive feedback, and intuitive control, leading to increased cognitive load and reduced performance. Extended Reality (XR), encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), presents a promising paradigm shift for RHRI by offering enhanced immersion, rich sensory feedback, and natural interaction modalities. This systematic literature review comprehensively analyzes the current landscape of XR-enabled RHRI systems. We categorize existing approaches by XR technology (VR, AR, MR), examine various interaction modalities (visual, haptic, auditory, gesture, eye-tracking), and explore key application domains such as industrial manufacturing, hazardous environments, medical, and space exploration. The review highlights the significant benefits of XR in improving situational awareness, control intuition, and collaborative capabilities. Furthermore, it identifies persistent challenges, including latency, field of view limitations, usability, and hardware constraints. Finally, we discuss critical future research directions, emphasizing adaptive interfaces, standardization, enhanced multimodal feedback, and long-term human factors studies, to foster more efficient, safer, and natural human-robot interactions in remote settings.


Keywords

Extended Reality, Virtual Reality, Augmented Reality, Mixed Reality

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How to Cite

Immersive Technologies for Remote Human-Robot Interaction: A Systematic Literature Review. (2024). European Journals of Emerging Computer Vision and Natural Language Processing, 1(01), 1-20. https://www.parthenonfrontiers.com/index.php/ejecvnlp/article/view/74

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