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Robotics
Special Issues
Immersive Teleoperation and AI
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Immersive Teleoperation and AI

A special issue of Robotics (ISSN 2218-6581). This special issue belongs to the section "AI in Robotics".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 10072

Special Issue Editors

Dr. Salvatore Livatino

E-Mail Website
Guest Editor
School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, UK
Interests: Immersive technologies; 3D visualization; photo-based VR, XR interfaces for telepresence; teleoperation and command and control.
Special Issues, Collections and Topics in MDPI journals
Dr. Dario Calogero Guastella

E-Mail Website
Guest Editor
Department of Electrical, Electronic and Computer Engineering, University of Catania, 95131 Catania, Italy
Interests: control and navigation of autonomous robots; aerial and ground robots cooperation; artificial intelligence for autonomous navigation in challenging environments
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lucio Tommaso De Paolis

E-Mail Website
Guest Editor
Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy
Interests: extended reality technologies in medicine; education and cultural heritage
Special Issues, Collections and Topics in MDPI journals
Dr. Daniele Ravi

E-Mail Website
Guest Editor
School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, UK
Interests: machine learning and AI; medical imaging; image-guided surgery; disease progression modeling and smart sensing

Special Issue Information

Dear Colleagues,

The past decade has brought us remarkable progress in immersive technologies widening the audience and applications of the extended reality (XR) domain, including virtual reality (VR), augmented reality (AR) and mixed reality (MR), with those technologies confirming their extraordinary power in supporting telepresence and robot teleoperation.

Immersive and VR/XR technologies are capable of dramatically increasing users’ performance when exploring and operating in remote environments, such as in telerobotics applications. Additionally, artificial intelligence methods, including machine learning and deep learning, have now become powerful enough to support visualization aspects and the decision-making process in teleoperation tasks.

In this Special Issue, you are invited to submit contributions describing novel approaches to the use of virtual reality, augmented reality and mixed reality, related to the tasks and application of telepresence and teleoperation, with particular attention to robotic applications. Contributions may also focus on applying novel artificial intelligence methods to this type of context and teleoperation.

Potential topics include but are not limited to the following:

  • VR/AR/MR for robotic teleoperation;
  • Immersive telepresence and systems;
  • User interfaces for remote monitoring and intervention;
  • Immersive human–machine interaction;
  • Immersive robot navigation and tele-control;
  • Medical robotics and VR/AR applications in healthcare;
  • VR/AR and 3D vision for remote surgery and medical intervention;
  • Image analysis for teleoperation;
  • AI-assisted tele-control;
  • AI-enhanced sensor data for teleoperation.

Prof. Dr. Salvatore Livatino
Dr. Dario Guastella
Prof. Dr. Lucio Tommaso De Paolis
Dr. Daniele Ravi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Robotics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

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19 pages, 4561 KiB  
Article
Design and Evaluation of an Intuitive Haptic Teleoperation Control System for 6-DoF Industrial Manipulators
by Ivo Dekker, Karel Kellens and Eric Demeester
Robotics 2023, 12(2), 54; https://doi.org/10.3390/robotics12020054 - 01 Apr 2023
Cited by 4 | Viewed by 2260
Abstract
Industrial robots are capable of performing automated tasks repeatedly, reliably and accurately. However, in some scenarios, human-in-the-loop control is required. In this case, having an intuitive system for moving the robot within the working environment is crucial. Additionally, the operator should be aided [...] Read more.
Industrial robots are capable of performing automated tasks repeatedly, reliably and accurately. However, in some scenarios, human-in-the-loop control is required. In this case, having an intuitive system for moving the robot within the working environment is crucial. Additionally, the operator should be aided by sensory feedback to obtain a user-friendly robot control system. Haptic feedback is one way of achieving such a system. This paper designs and assesses an intuitive teleoperation system for controlling an industrial 6-DoF robotic manipulator using a Geomagic Touch haptic interface. The system utilises both virtual environment-induced and physical sensor-induced haptic feedback to provide the user with both a higher amount of environmental awareness and additional safety while manoeuvering the robot within its working area. Different tests show that the system is capable of fully stopping the manipulator without colliding with the environment, and preventing it from entering singularity states with Cartesian end effector velocities of up to 0.25 m/s. Additionally, an operator is capable of executing low-tolerance end effector positioning tasks (∼0.5 mm) with high-frequency control of the robot (∼100 Hz). Fourteen inexperienced volunteers were asked to perform a typical object removal and writing task to gauge the intuitiveness of the system. It was found that when repeating the same test for a second time, the participants performed 22.2% faster on average. The results for the second attempt also became significantly more consistent between participants, as the inter quartile range dropped by 82.7% (from 52 s on the first attempt to 9 s on the second). Full article
(This article belongs to the Special Issue Immersive Teleoperation and AI)
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15 pages, 3917 KiB  
Article
Design of a Novel Haptic Joystick for the Teleoperation of Continuum-Mechanism-Based Medical Robots
by Yiping Xie, Xilong Hou and Shuangyi Wang
Robotics 2023, 12(2), 52; https://doi.org/10.3390/robotics12020052 - 29 Mar 2023
Cited by 2 | Viewed by 2275
Abstract
Continuum robots are increasingly used in medical applications and the master–slave-based architectures are still the most important mode of operation in human–machine interaction. However, the existing master control devices are not fully suitable for either the mechanical mechanism or the control method. This [...] Read more.
Continuum robots are increasingly used in medical applications and the master–slave-based architectures are still the most important mode of operation in human–machine interaction. However, the existing master control devices are not fully suitable for either the mechanical mechanism or the control method. This study proposes a brand-new, four-degree-of-freedom haptic joystick whose main control stick could rotate around a fixed point. The rotational inertia is reduced by mounting all powertrain components on the base plane. Based on the design, kinematic and static models are proposed for position perception and force output analysis, while at the same time gravity compensation is also performed to calibrate the system. Using a continuum-mechanism-based trans-esophageal ultrasound robot as the test platform, a master–slave teleoperation scheme with position–velocity mapping and variable impedance control is proposed to integrate the speed regulation on the master side and the force perception on the slave side. The experimental results show that the main accuracy of the design is within 1.6°. The workspace of the control sticks is −60° to 110° in pitch angle, −40° to 40° in yaw angle, −180° to 180° in roll angle, and −90° to 90° in translation angle. The standard deviation of force output is within 8% of the full range, and the mean absolute error is 1.36°/s for speed control and 0.055 N for force feedback. Based on this evidence, it is believed that the proposed haptic joystick is a good addition to the existing work in the field with well-developed and effective features to enable the teleoperation of continuum robots for medical applications. Full article
(This article belongs to the Special Issue Immersive Teleoperation and AI)
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Review

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23 pages, 645 KiB  
Review
Recent Advances and Perspectives in Deep Learning Techniques for 3D Point Cloud Data Processing
by Zifeng Ding, Yuxuan Sun, Sijin Xu, Yan Pan, Yanhong Peng and Zebing Mao
Robotics 2023, 12(4), 100; https://doi.org/10.3390/robotics12040100 - 11 Jul 2023
Cited by 4 | Viewed by 4207
Abstract
In recent years, deep learning techniques for processing 3D point cloud data have seen significant advancements, given their unique ability to extract relevant features and handle unstructured data. These techniques find wide-ranging applications in fields like robotics, autonomous vehicles, and various other computer-vision [...] Read more.
In recent years, deep learning techniques for processing 3D point cloud data have seen significant advancements, given their unique ability to extract relevant features and handle unstructured data. These techniques find wide-ranging applications in fields like robotics, autonomous vehicles, and various other computer-vision applications. This paper reviews the recent literature on key tasks, including 3D object classification, tracking, pose estimation, segmentation, and point cloud completion. The review discusses the historical development of these methods, explores different model architectures, learning algorithms, and training datasets, and provides a comprehensive summary of the state-of-the-art in this domain. The paper presents a critical evaluation of the current limitations and challenges in the field, and identifies potential areas for future research. Furthermore, the emergence of transformative methodologies like PoinTr and SnowflakeNet is examined, highlighting their contributions and potential impact on the field. The potential cross-disciplinary applications of these techniques are also discussed, underscoring the broad scope and impact of these developments. This review fills a knowledge gap by offering a focused and comprehensive synthesis of recent research on deep learning techniques for 3D point cloud data processing, thereby serving as a useful resource for both novice and experienced researchers in the field. Full article
(This article belongs to the Special Issue Immersive Teleoperation and AI)
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