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Robotics
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Selected Papers from the 15th International Symposium on Applied Computational...
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Selected Papers from the 15th International Symposium on Applied Computational Intelligence and Informatics (SACI 2021)

A special issue of Robotics (ISSN 2218-6581). This special issue belongs to the section "Intelligent Robots and Mechatronics".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 10674

Special Issue Editors

Prof. Dr. Levente Adalbert Kovács

E-Mail Website
Guest Editor
PhysCon Lab., University Research and Innovation Center, Óbuda University, 1034 Budapest, Hungary
Interests: computer science; modeling and control of physiological systems; image processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Radu-Emil Precup

E-Mail Website
Guest Editor
Department of Automation and Applied Informatics, Faculty of Automation and Computers, Politehnica University of Timişoara, Bulevardul Vasile Pârvan, Nr. 2, 300223 Timişoara, Romania
Interests: new control structures and algorithms; soft computing; computer-aided design of control systems; modelling; optimization; mechatronic systems; embedded systems; control of power plants; servo systems; electrical driving systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

Technology development unfolds in megatrends these decades, transforming individuals and society alike. The Internet of Things, Cloud Robotics, Industry 4.0, and agile Cyber-Physical Systems show how these transformations occur. The fundamental engineering concepts behind this are rooted in academic research, often presented at the major scientific conferences of the community. This Special Issue is a collection of the newest research results based on the selected presentations at the 15th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI 2021).

With a focus on robot control and applications, articles across the broad field of computational intelligence are also welcome to be submitted to this special issue. Non-SACI papers are welcome on topics that are related but not limited to the following areas:

  • Robotics and AI;
  • Machine Learning and Computational Intelligence;
  • Intelligent Mechatronics;
  • Intelligent Manufacturing Systems;
  • Intelligent Robot Control.

Prof. Dr. Levente Kovács
Prof. Dr. Radu-Emil Precup
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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14 pages, 10239 KiB  
Article
Sensor Fusion with Deep Learning for Autonomous Classification and Management of Aquatic Invasive Plant Species
by Jackson E. Perrin, Shaphan R. Jernigan, Jacob D. Thayer, Andrew W. Howell, James K. Leary and Gregory D. Buckner
Robotics 2022, 11(4), 68; https://doi.org/10.3390/robotics11040068 - 28 Jun 2022
Cited by 4 | Viewed by 2109
Abstract
Recent advances in deep learning, including the development of AlexNet, Residual Network (ResNet), and transfer learning, offer unprecedented classification accuracy in the field of machine vision. A developing application of deep learning is the automated identification and management of aquatic invasive plants. Classification [...] Read more.
Recent advances in deep learning, including the development of AlexNet, Residual Network (ResNet), and transfer learning, offer unprecedented classification accuracy in the field of machine vision. A developing application of deep learning is the automated identification and management of aquatic invasive plants. Classification of submersed aquatic vegetation (SAV) presents a unique challenge, namely, the lack of a single source of sensor data that can produce robust, interpretable images across a variable range of depth, turbidity, and lighting conditions. This paper focuses on the development of a multi-sensor (RGB and hydroacoustic) classification system for SAV that is robust to environmental conditions and combines the strengths of each sensing modality. The detection of invasive Hydrilla verticillata (hydrilla) is the primary goal. Over 5000 aerial RGB and hydroacoustic images were generated from two Florida lakes via an unmanned aerial vehicle and boat-mounted sonar unit, and tagged for neural network training and evaluation. Classes included “HYDR”, containing hydrilla; “NONE”, lacking SAV, and “OTHER”, containing SAV other than hydrilla. Using a transfer learning approach, deep neural networks with the ResNet architecture were individually trained on the RGB and hydroacoustic datasets. Multiple data fusion methodologies were evaluated to ensemble the outputs of these neural networks for optimal classification accuracy. A method incorporating logic and a Monte Carlo dropout approach yielded the best overall classification accuracy (84%), with recall and precision of 84.5% and 77.5%, respectively, for the hydrilla class. The training and ensembling approaches were repeated for a DenseNet model with identical training and testing datasets. The overall classification accuracy was similar between the ResNet and DenseNet models when averaged across all approaches (1.9% higher accuracy for the ResNet vs. the DenseNet). Full article
(This article belongs to the Special Issue Selected Papers from the 15th International Symposium on Applied Computational Intelligence and Informatics (SACI 2021))
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21 pages, 6581 KiB  
Article
Teleoperation Control of an Underactuated Bionic Hand: Comparison between Wearable and Vision-Tracking-Based Methods
by Junling Fu, Massimiliano Poletti, Qingsheng Liu, Elisa Iovene, Hang Su, Giancarlo Ferrigno and Elena De Momi
Robotics 2022, 11(3), 61; https://doi.org/10.3390/robotics11030061 - 14 May 2022
Cited by 8 | Viewed by 5051
Abstract
Bionic hands have been employed in a wide range of applications, including prosthetics, robotic grasping, and human–robot interaction. However, considering the underactuated and nonlinear characteristics, as well as the mechanical structure’s backlash, achieving natural and intuitive teleoperation control of an underactuated bionic hand [...] Read more.
Bionic hands have been employed in a wide range of applications, including prosthetics, robotic grasping, and human–robot interaction. However, considering the underactuated and nonlinear characteristics, as well as the mechanical structure’s backlash, achieving natural and intuitive teleoperation control of an underactuated bionic hand remains a critical issue. In this paper, the teleoperation control of an underactuated bionic hand using wearable and vision-tracking system-based methods is investigated. Firstly, the nonlinear behaviour of the bionic hand is observed and the kinematics model is formulated. Then, the wearable-glove-based and the vision-tracking-based teleoperation control frameworks are implemented, respectively. Furthermore, experiments are conducted to demonstrate the feasibility and performance of these two methods in terms of accuracy in both static and dynamic scenarios. Finally, a user study and demonstration experiments are conducted to verify the performance of these two approaches in grasp tasks. Both developed systems proved to be exploitable in both powered and precise grasp tasks using the underactuated bionic hand, with a success rate of 98.6% and 96.5%, respectively. The glove-based method turned out to be more accurate and better performing than the vision-based one, but also less comfortable, requiring greater effort by the user. By further incorporating a robot manipulator, the system can be utilised to perform grasp, delivery, or handover tasks in daily, risky, and infectious scenarios. Full article
(This article belongs to the Special Issue Selected Papers from the 15th International Symposium on Applied Computational Intelligence and Informatics (SACI 2021))
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Review

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15 pages, 2840 KiB  
Review
What Is Next in Computer-Assisted Spine Surgery? Advances in Image-Guided Robotics and Extended Reality
by Kristóf Móga, Andrea Ferencz and Tamás Haidegger
Robotics 2023, 12(1), 1; https://doi.org/10.3390/robotics12010001 - 20 Dec 2022
Cited by 6 | Viewed by 2462
Abstract
Background: This article provides a scoping review on the current status of Image-Guided Navigation with various forms of digital technologies, including Extended Reality, Augmented Reality Head-Mounted Displays (AR–HMDs) and Robot-Assisted Surgery (RAS) for Pedicle Screw Placement in orthopedics and spine surgery. Methods: A [...] Read more.
Background: This article provides a scoping review on the current status of Image-Guided Navigation with various forms of digital technologies, including Extended Reality, Augmented Reality Head-Mounted Displays (AR–HMDs) and Robot-Assisted Surgery (RAS) for Pedicle Screw Placement in orthopedics and spine surgery. Methods: A scoping literature review was performed in the PubMed, Scopus, Embase, Web of Science, Google Scholar and IEEE Xplore databases to collect clinical and user satisfaction data on AR–HMDs and compare those with RAS outcomes. In vivo patient, cadaver and phantom trial accuracy data reports were identified and grouped through the analysis. Over the past two years, 14 publications were retrieved and analyzed. Pedicle screw placement accuracy was described with Linear Tip Error (LTE), Angular Trajectory Error (ATE) and Gertzbein–Robbins Scale (GRS) outcomes. Results: The Pedicle Screw Placement accuracy was seen to increase in the in vivo, cadaver and phantom model groups using AR-HMD compared to the Free-Hand insertion technique. User experience and satisfaction data were limited; however, a clear advantage for the operative results was described when it was added. RAS screwing showed similar accuracy outcomes. The need for benchmarking and quantified situation awareness for AR–HMDs is recognizable. The authors present a method for standardized scoring and visualization of surgical navigation technologies, based on measurements of the surgeon (as the end-users) user satisfaction, clinical accuracy and operation time. Conclusions: computer-technology driven support for spine surgery is well-established and efficient for certain procedures. As a more affordable option next to RAS, AR–HMD navigation has reached technological readiness for surgical use. Ergonomics and usability improvements are needed to match the potential of RAS/XR in human surgeries. Full article
(This article belongs to the Special Issue Selected Papers from the 15th International Symposium on Applied Computational Intelligence and Informatics (SACI 2021))
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