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Sensing for Robotics and Automation
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Sensing for Robotics and Automation

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 10 May 2024 | Viewed by 18336

Special Issue Editors

Prof. Dr. Igor Korobiichuk

E-Mail Website
Guest Editor
Institute of Automatic Control and Robotics, Warsaw University of Technology, 02-525 Warsaw, Poland
Interests: sensors; measurement systems; control and instrumentation; inertial navigation sensors; MEMS accelerometers; mechatronics; system analysis
Dr. Michał Nowicki

E-Mail Website
Guest Editor
Institute of Metrology and Biomedical Engineering, Warsaw University of Technology, Warsaw, Poland
Interests: magnetic sensors; magnetic materials; magnetomechanical effect; measurement systems; force sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Robots use a large number of sensors to achieve good operation and control in automation production processes. With the drive toward “Industry 4.0”, the use of robotics and automation has become commonplace as they allow increased efficiency and precision. Therefore, the development of new sensors and measurement systems for robotics and automation requires new solutions that enable accurate, safe, and cost-effective operation.

This Special Issue seeks to showcase reviews or rigorous original papers focused on remote sensing via UAVs (unmanned aerial vehicles); tactile sensing and sound sensors for robots; state of the art in automated tactile sensing; target tracking, including multiple targets with multiple sensors; visual sensing in robotics and automation; applications of robot sensing; multi-sensing automated systems;  all new solutions of sensing systems for robotics and automation control of robotics. Potential topics include, but are not limited to, the following:

  • Robotics
  • Measurement system
  • Mobile robotics
  • Sensors
  • UAV
  • Inertial navigation systems
  • Tracking control
  • Automatic control

Prof. Dr. Igor Korobiichuk
Dr. Michał Nowicki
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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 (4 papers)

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Research

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18 pages, 15418 KiB  
Article
A Robotics Experimental Design Method Based on PDCA: A Case Study of Wall-Following Robots
by Kai-Yi Wong, Shuai-Cheng Pu and Ching-Chang Wong
Sensors 2024, 24(6), 1869; https://doi.org/10.3390/s24061869 - 14 Mar 2024
Viewed by 590
Abstract
There is a lack of research that proposes a complete and interoperable robotics experimental design method to improve students’ learning outcomes. Therefore, this study proposes a student-oriented method based on the plan-do-check-act (PDCA) concept to design robotics experiments. The proposed method is based [...] Read more.
There is a lack of research that proposes a complete and interoperable robotics experimental design method to improve students’ learning outcomes. Therefore, this study proposes a student-oriented method based on the plan-do-check-act (PDCA) concept to design robotics experiments. The proposed method is based on our teaching experience and multiple practical experiences of allowing students to do hands-on experiments. It consists of eight steps, mainly including experimental goals, experimental activities, robot assembly, robot control, in-class evaluation criteria, and after-class report requirements. The after-class report requirements designed in the proposed method can help students improve their report-writing abilities. A wall-following robotics experiment designed using the PDCA method is proposed, and some students’ learning outcomes and after-class reports in this experiment are presented to illustrate the effectiveness of the proposed method. This experiment also helps students to understand the fundamental application of multi-sensor fusion technology in designing an autonomous mobile robot. We can see that the proposed reference examples allow students to quickly assemble two-wheeled mobile robots with four different sensors and to design programs to control these assembled robots. In addition, the proposed in-class evaluation criteria stimulate students’ creativity in assembling different wall-following robots or designing different programs to achieve this experiment. We present the learning outcomes of three stages of the wall-following robotics experiment. Three groups of 42, 37, and 44 students participated in the experiment in these three stages, respectively. The ratios of the time required for the robots designed by students to complete the wall-following experiment, less than that of the teaching example, are 3/42 = 7.14%, 26/37 = 70.27%, and 44/44 = 100%, respectively. From the comparison of learning outcomes in the three stages, it can be seen that the proposed PDCA-based design method can indeed improve students’ learning outcomes and stimulate their active learning and creativity. Full article
(This article belongs to the Special Issue Sensing for Robotics and Automation)
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32 pages, 16437 KiB  
Article
Spatially Resolved Analysis of Urban Thermal Environments Based on a Three-Dimensional Sampling Algorithm and UAV-Based Radiometric Measurements
by Daniel Rüdisser, Tobias Weiss and Lukas Unger
Sensors 2021, 21(14), 4847; https://doi.org/10.3390/s21144847 - 16 Jul 2021
Cited by 7 | Viewed by 2824
Abstract
A new method and workflow to assess outdoor thermal comfort and thermal stress in urban areas is developed. The new methodology is applied to a case of an urban quarter in the city of Graz. The method recognises the significance of detailed and [...] Read more.
A new method and workflow to assess outdoor thermal comfort and thermal stress in urban areas is developed. The new methodology is applied to a case of an urban quarter in the city of Graz. The method recognises the significance of detailed and accurate spatially resolved determination of mean radiant temperatures taking into account all relevant radiative components, comprising thermal radiation, as well as global radiation. The method relies on radiometric imaging data that are mapped onto a three-dimensional model. The image data are acquired by means of drones (UAVs) equipped with multispectral and thermographic cameras to capture short- and long-wave radiation. Pre-existing city models and a Monte Carlo raytracing algorithm to perform anisotropic sampling based on a 3D model with human topology are used to determine local radiation temperatures with high spatial resolution. Along with spot measurements carried out on the ground simultaneously, the spatially resolved and three-dimensionally determined mean radiation temperatures are used to calculate thermal comfort indicator maps using UTCI and PMV calculation. Additional ground measurements are further used to validate the detection, as well as the entire evaluation process. Full article
(This article belongs to the Special Issue Sensing for Robotics and Automation)
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Review

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30 pages, 426 KiB  
Review
A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance
by Lucas C. F. Domingos, Paulo E. Santos, Phillip S. M. Skelton, Russell S. A. Brinkworth and Karl Sammut
Sensors 2022, 22(6), 2181; https://doi.org/10.3390/s22062181 - 11 Mar 2022
Cited by 18 | Viewed by 7794
Abstract
This paper presents a comprehensive overview of current deep-learning methods for automatic object classification of underwater sonar data for shoreline surveillance, concentrating mostly on the classification of vessels from passive sonar data and the identification of objects of interest from active sonar (such [...] Read more.
This paper presents a comprehensive overview of current deep-learning methods for automatic object classification of underwater sonar data for shoreline surveillance, concentrating mostly on the classification of vessels from passive sonar data and the identification of objects of interest from active sonar (such as minelike objects, human figures or debris of wrecked ships). Not only is the contribution of this work to provide a systematic description of the state of the art of this field, but also to identify five main ingredients in its current development: the application of deep-learning methods using convolutional layers alone; deep-learning methods that apply biologically inspired feature-extraction filters as a preprocessing step; classification of data from frequency and time–frequency analysis; methods using machine learning to extract features from original signals; and transfer learning methods. This paper also describes some of the most important datasets cited in the literature and discusses data-augmentation techniques. The latter are used for coping with the scarcity of annotated sonar datasets from real maritime missions. Full article
(This article belongs to the Special Issue Sensing for Robotics and Automation)
36 pages, 1094 KiB  
Review
Spherical Robots for Special Purposes: A Review on Current Possibilities
by Marek Bujňák, Rastislav Pirník, Karol Rástočný, Aleš Janota, Dušan Nemec, Pavol Kuchár, Tomáš Tichý and Zbigniew Łukasik
Sensors 2022, 22(4), 1413; https://doi.org/10.3390/s22041413 - 12 Feb 2022
Cited by 23 | Viewed by 5258
Abstract
The review discusses the possibilities of different driving mechanisms and sensors of spherical robots, and a special kind of mobile robots is introduced and discussed. The sensors discussed can expand robots’ sensing capabilities which are typically very limited. Most spherical robots have holonomic [...] Read more.
The review discusses the possibilities of different driving mechanisms and sensors of spherical robots, and a special kind of mobile robots is introduced and discussed. The sensors discussed can expand robots’ sensing capabilities which are typically very limited. Most spherical robots have holonomic characteristics and protect the inner environment using a shell. Today, there are a diversity of driving mechanisms. Therefore, this article provides a review of all of them and identifies their basic properties. Accordingly, many spherical robots have only inner sensors for moving, balancing, driving, etc. However, a few of them are also equipped with sensors that can measure environmental properties. Therefore, in this paper, we propose the possibility of using such sensors as cameras, LiDARs, thermocouples, and gas sensors, which can be used for special purposes underground, for example, in mines, underground tunnels, or road tunnels. After combining all components are combined, it is possible to design a special type of spherical robot designed for underground exploration, such as accidents in mines or road tunnels. Full article
(This article belongs to the Special Issue Sensing for Robotics and Automation)
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