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Machines
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State-of-Art in Sensors for Robotic Applications
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State-of-Art in Sensors for Robotic Applications

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 13341

Special Issue Editors

Prof. Dr. Gianluca Palli

E-Mail Website
Guest Editor
Department of Electrical, Electronic and Information Engineering, Università di Bologna—DEI, Viale del Risorgimento 2, 40136 Bologna, Italy
Interests: robotic manipulation; robotic hands; design and control of robotic manipulators; underwater robotics; force/tactile sensors; compliant actuation; mobile manipulation; manipulation of deformable objects
Special Issues, Collections and Topics in MDPI journals
Dr. Salvatore Pirozzi

E-Mail Website
Guest Editor
Dipartimento di Ingegneria, Università degli Studi della Campania “Luigi Vanvitelli”, Via Roma, 29, 81031 Aversa, CE, Italy
Interests: sensors for robotics applications; control; human–robot interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In many robotic applications, the successful execution of a task strongly depends on the knowledge of environment features. For example, in a grasping and manipulation task, the objects’ geometrical and physical characteristics produce fundamental information for a possible automatization. For the implementation of a human–robot collaborative task, the interaction is possible only through suitable sensing systems. To address these issues, but also for many other cases, robotic systems are frequently equipped with a high number of sensing devices, both standard and innovative, which measurements are used as inputs both for model-based control systems and machine learning systems. The latter systems are developed more and more, in order to increase the autonomy level of the robots. This Special Issue will present how sensing systems integrated in robotic systems can be used with recent techniques of sensor fusion, in order to allow the automatization of new challenging tasks.

This Special Issue invites (but is not limited to) contributions on the following topics:

  • Sensor technologies for robotic applications
  • Sensor modeling for robotic applications
  • Data interpretation for robotic applications
  • Grasping and manipulation
  • Dexterous manipulation
  • Object features recognition for robotic applications
  • Physical human–robot interactions
  • Human–machine interfaces

You may choose our Joint Special Issue in Sensors.

Dr. Gianluca Palli
Prof. Dr. Salvatore Pirozzi
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. Machines 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 2400 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.

Keywords

  • Sensor technologies for robotic applications
  • Sensor modelling for robotic applications
  • Data interpretation for robotic applications
  • Grasping and manipulation
  • Dexterous manipulation
  • Object features recognition for robotic applications
  • Physical human robot interaction
  • Human machine interfaces

Related Special Issue

Published Papers (4 papers)

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Research

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15 pages, 2750 KiB  
Article
Localization and Mapping for UGV in Dynamic Scenes with Dynamic Objects Eliminated
by Junsong Li and Jilin He
Machines 2022, 10(11), 1044; https://doi.org/10.3390/machines10111044 - 08 Nov 2022
Cited by 2 | Viewed by 1392
Abstract
SLAM (Simultaneous Localization and Mapping) based on lidar is an important method for UGV (Unmanned Ground Vehicle) localization in real time under GNSS (Global Navigation Satellite System)-denied situations. However, dynamic objects in real-world scenarios affect odometry in SLAM and reduce localization accuracy. We [...] Read more.
SLAM (Simultaneous Localization and Mapping) based on lidar is an important method for UGV (Unmanned Ground Vehicle) localization in real time under GNSS (Global Navigation Satellite System)-denied situations. However, dynamic objects in real-world scenarios affect odometry in SLAM and reduce localization accuracy. We propose a novel lidar SLAM algorithm based on LOAM (Lidar Odometry and Mapping), which is popular in this field. First, we applied elevation maps to label the ground point cloud. Then we extracted convex hulls in point clouds based on scanlines as materials for dynamic object clustering. We replaced these dynamic objects with background point cloud to avoid accuracy reduction. Finally, we extracted feature points from ground points and non-ground points, respectively, and matched these feature points frame-to-frame to estimate ground robot motion. We evaluated the proposed algorithm in dynamic industrial park roads, and it kept UGV maximum relative position error less than 3% and average relative position error less than 2%. Full article
(This article belongs to the Special Issue State-of-Art in Sensors for Robotic Applications)
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24 pages, 77464 KiB  
Article
Proximity Sensor for Thin Wire Recognition and Manipulation
by Andrea Cirillo, Gianluca Laudante and Salvatore Pirozzi
Machines 2021, 9(9), 188; https://doi.org/10.3390/machines9090188 - 03 Sep 2021
Cited by 3 | Viewed by 3023
Abstract
In robotic grasping and manipulation, the knowledge of a precise object pose represents a key issue. The point acquires even more importance when the objects and, then, the grasping areas become smaller. This is the case of Deformable Linear Object manipulation application where [...] Read more.
In robotic grasping and manipulation, the knowledge of a precise object pose represents a key issue. The point acquires even more importance when the objects and, then, the grasping areas become smaller. This is the case of Deformable Linear Object manipulation application where the robot shall autonomously work with thin wires which pose and shape estimation could become difficult given the limited object size and possible occlusion conditions. In such applications, a vision-based system could not be enough to obtain accurate pose and shape estimation. In this work the authors propose a Time-of-Flight pre-touch sensor, integrated with a previously designed tactile sensor, for an accurate estimation of thin wire pose and shape. The paper presents the design and the characterization of the proposed sensor. Moreover, a specific object scanning and shape detection algorithm is presented. Experimental results support the proposed methodology, showing good performance. Hardware design and software applications are freely accessible to the reader. Full article
(This article belongs to the Special Issue State-of-Art in Sensors for Robotic Applications)
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17 pages, 53276 KiB  
Article
Frequency Measurement Method of Signals with Low Signal-to-Noise-Ratio Using Cross-Correlation
by Yang Liu, Jigou Liu and Ralph Kennel
Machines 2021, 9(6), 123; https://doi.org/10.3390/machines9060123 - 18 Jun 2021
Cited by 4 | Viewed by 3863
Abstract
Precise frequency measurement plays an essential role in many industrial and robotic systems. However, different effects in the application’s environment cause signal noises, which make frequency measurement more difficult. In small signals or rough environments, even negative Signal-to-Noise Ratios (SNRs) are possible. Thus, [...] Read more.
Precise frequency measurement plays an essential role in many industrial and robotic systems. However, different effects in the application’s environment cause signal noises, which make frequency measurement more difficult. In small signals or rough environments, even negative Signal-to-Noise Ratios (SNRs) are possible. Thus, frequency measuring methods, which are suited for low SNR signals, are in great demand. While denoising methods such as autocorrelation do not suffice for small signal with low SNR, frequency measurement methods such as Fast-Fourier Transformation or Continuous Wavelet Transformation suffer from Heisenberg’s uncertainty principle, which makes simultaneous high frequency and time resolutions impossible. In this paper, the cross-correlation spectrum is presented as a new frequency measuring method. It can be used in any frequency domain, and provides greater denoising than autocorrelation. Furthermore, frequency and time resolutions are independent from one another, and can be set separately by the user. In simulations, it achieves an average deviation of less than 0.1% on sinusoidal signals with a SNR of −10 dB and a signal length of 1000 data points. When applied to “self-mixing”-interferometry signals, the method can reach a normalized root-mean square error of 0.2% with the aid of an estimation method and an averaging algorithm. Therefore, further research of the method is recommended. Full article
(This article belongs to the Special Issue State-of-Art in Sensors for Robotic Applications)
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Review

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17 pages, 2866 KiB  
Review
A Review of Sensors Used on Fabric-Handling Robots
by Petros I. Kaltsas, Panagiotis N. Koustoumpardis and Pantelis G. Nikolakopoulos
Machines 2022, 10(2), 101; https://doi.org/10.3390/machines10020101 - 28 Jan 2022
Cited by 4 | Viewed by 4083
Abstract
While in most industries, most processes are automated and human workers have either been replaced by robots or work alongside them, fewer changes have occurred in industries that use limp materials, like fabrics, clothes, and garments, than might be expected with today’s technological [...] Read more.
While in most industries, most processes are automated and human workers have either been replaced by robots or work alongside them, fewer changes have occurred in industries that use limp materials, like fabrics, clothes, and garments, than might be expected with today’s technological evolution. Integration of robots in these industries is a relatively demanding and challenging task, mostly because of the natural and mechanical properties of limp materials. In this review, information on sensors that have been used in fabric-handling applications is gathered, analyzed, and organized based on criteria such as their working principle and the task they are designed to support. Categorization and related works are presented in tables and figures so someone who is interested in developing automated fabric-handling applications can easily get useful information and ideas, at least regarding the necessary sensors for the most common handling tasks. Finally, we hope this work will inspire researchers to design new sensor concepts that could promote automation in the industry and boost the robotization of domestic chores involving with flexible materials. Full article
(This article belongs to the Special Issue State-of-Art in Sensors for Robotic Applications)
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