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Special Issue "Sensors for Robotic Applications"

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

Deadline for manuscript submissions: 31 December 2022.

Special Issue Editors

Prof. Dr. Rezia Molfino
E-Mail Website
Guest Editor
PMAR Group, Department of Mechanics and Machine Design, University of Genova, Via all’Opera 15A, 16145 Genova, Italy
Interests: robotics and industrial automation; control; simulation
Dr. Francesco Cepolina
E-Mail Website
Guest Editor
PMAR Group, Department of Mechanics and Machine Design, University of Genova, Via all’Opera 15A, 16145 Genova, Italy
Interests: surgical robotics and industrial automation; sensors for quality control; smart mechanisms design; simulation

Special Issue Information

Dear Colleagues,

Today, robots not only work in structured, well-known environments to perform repetitive tasks precisely and accurately; they also need to know information about themselves through the use of proprioceptive sensors on which the control feedbacks close.

The digital transformation of industry allows the continuous adjustment and reaction to unforeseen disturbing events of production systems through digital twin and similar technologies, and requires the intelligent sensorization of robots and resources for remote monitoring.

Today, robots are increasingly playing a role in our daily life and in civil society. They are called upon to do housework, to help and extend the social life of the elderly and the weak, to protect the environment, to keep worrying climatic events under control, and to help workers and people in a variety of tasks.

In new industrial and service applications, robots have to become familiar with and interact with the environments in which they operate and cooperate.

Robots are sometimes required to carry out, remotely supervised or autonomously, typical human tasks in extreme environments that are hostile and dangerous. Therefore, they must be endowed with the inspection, measuring, and testing capabilities typical of that task.

These applications require the heavy use of exteroceptive sensors and perceptive skills to recognize the environment and guide interaction activities, safeguarding the sustainability of the environment and human health.

The aim of this Special Issue is to collect experimental and theoretical papers covering different aspects and modalities of sensing applications in the wide industrial and service robotics domain.

Prof. Dr. Rezia Molfino
Dr. Francesco Cepolina
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 papers will be 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 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

  • accuracy
  • interaction
  • sustainability
  • inspection
  • proprioceptive sensors
  • exteroceptive sensors
  • intelligent sensorization

Published Papers (5 papers)

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Research

Article
The Implementation and Evaluation of Individual Preference in Robot Facial Expression Based on Emotion Estimation Using Biological Signals
Sensors 2021, 21(18), 6322; https://doi.org/10.3390/s21186322 - 21 Sep 2021
Viewed by 597
Abstract
Recently, robot services have been widely applied in many fields. To provide optimum service, it is essential to maintain good acceptance of the robot for more effective interaction with users. Previously, we attempted to implement facial expressions by synchronizing an estimated human emotion [...] Read more.
Recently, robot services have been widely applied in many fields. To provide optimum service, it is essential to maintain good acceptance of the robot for more effective interaction with users. Previously, we attempted to implement facial expressions by synchronizing an estimated human emotion on the face of a robot. The results revealed that the robot could present different perceptions according to individual preferences. In this study, we considered individual differences to improve the acceptance of the robot by changing the robot’s expression according to the emotion of its interacting partner. The emotion was estimated using biological signals, and the robot changed its expression according to three conditions: synchronized with the estimated emotion, inversely synchronized, and a funny expression. During the experiment, the participants provided feedback regarding the robot’s expression by choosing whether they “like” or “dislike” the expression. We investigated individual differences in the acceptance of the robot expression using the Semantic Differential scale method. In addition, logistic regression was used to create a classification model by considering individual differences based on the biological data and feedback from each participant. We found that the robot expression based on inverse synchronization when the participants felt a negative emotion could result in impression differences among individuals. Then, the robot’s expression was determined based on the classification model, and the Semantic Differential scale on the impression of the robot was compared with the three conditions. Overall, we found that the participants were most accepting when the robot expression was calculated using the proposed personalized method. Full article
(This article belongs to the Special Issue Sensors for Robotic Applications)
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Article
Human–Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot
Sensors 2021, 21(17), 5882; https://doi.org/10.3390/s21175882 - 31 Aug 2021
Viewed by 1050
Abstract
People with severe disabilities require assistance to perform their routine activities; a Human–Machine Interface (HMI) will allow them to activate devices that respond according to their needs. In this work, an HMI based on electrooculography (EOG) is presented, the instrumentation is placed on [...] Read more.
People with severe disabilities require assistance to perform their routine activities; a Human–Machine Interface (HMI) will allow them to activate devices that respond according to their needs. In this work, an HMI based on electrooculography (EOG) is presented, the instrumentation is placed on portable glasses that have the task of acquiring both horizontal and vertical EOG signals. The registration of each eye movement is identified by a class and categorized using the one hot encoding technique to test precision and sensitivity of different machine learning classification algorithms capable of identifying new data from the eye registration; the algorithm allows to discriminate blinks in order not to disturb the acquisition of the eyeball position commands. The implementation of the classifier consists of the control of a three-wheeled omnidirectional robot to validate the response of the interface. This work proposes the classification of signals in real time and the customization of the interface, minimizing the user’s learning curve. Preliminary results showed that it is possible to generate trajectories to control an omnidirectional robot to implement in the future assistance system to control position through gaze orientation. Full article
(This article belongs to the Special Issue Sensors for Robotic Applications)
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Article
A Soft Tactile Sensor Based on Magnetics and Hybrid Flexible-Rigid Electronics
Sensors 2021, 21(15), 5098; https://doi.org/10.3390/s21155098 - 28 Jul 2021
Viewed by 730
Abstract
Tactile sensing is crucial for robots to manipulate objects successfully. However, integrating tactile sensors into robotic hands is still challenging, mainly due to the need to cover small multi-curved surfaces with several components that must be miniaturized. In this paper, we report the [...] Read more.
Tactile sensing is crucial for robots to manipulate objects successfully. However, integrating tactile sensors into robotic hands is still challenging, mainly due to the need to cover small multi-curved surfaces with several components that must be miniaturized. In this paper, we report the design of a novel magnetic-based tactile sensor to be integrated into the robotic hand of the humanoid robot Vizzy. We designed and fabricated a flexible 4 × 2 matrix of Si chips of magnetoresistive spin valve sensors that, coupled with a single small magnet, can measure contact forces from 0.1 to 5 N on multiple locations over the surface of a robotic fingertip; this design is innovative with respect to previous works in the literature, and it is made possible by careful engineering and miniaturization of the custom-made electronic components that we employ. In addition, we characterize the behavior of the sensor through a COMSOL simulation, which can be used to generate optimized designs for sensors with different geometries. Full article
(This article belongs to the Special Issue Sensors for Robotic Applications)
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Article
Improving the Angular Velocity Measured with a Low-Cost Magnetic Rotary Encoder Attached to a Brushed DC Motor by Compensating Magnet and Hall-Effect Sensor Misalignments
Sensors 2021, 21(14), 4763; https://doi.org/10.3390/s21144763 - 12 Jul 2021
Cited by 2 | Viewed by 832
Abstract
This paper proposes a method to improve the angular velocity measured by a low-cost magnetic rotary encoder attached to a brushed direct current (DC) motor. The low-cost magnetic rotary encoder used in brushed DC motors use to have a small magnetic ring attached [...] Read more.
This paper proposes a method to improve the angular velocity measured by a low-cost magnetic rotary encoder attached to a brushed direct current (DC) motor. The low-cost magnetic rotary encoder used in brushed DC motors use to have a small magnetic ring attached to the rotational axis and one or more fixed Hall-effect sensors next to the magnet. Then, the Hall-effect sensors provide digital pulses with a duration and frequency proportional to the angular rotational velocity of the shaft of the encoder. The drawback of this mass produced rotary encoder is that any structural misalignment between the rotating magnetic field and the Hall-effect sensors produces asymmetric pulses that reduces the precision of the estimation of the angular velocity. The hypothesis of this paper is that the information provided by this low-cost magnetic rotary encoder can be processed and improved in order to obtain an accurate and precise estimation of the angular rotational velocity. The methodology proposed has been validated in four compact motorizations obtaining a reduction in the ripple of the estimation of the angular rotational velocity of: 4.93%, 59.43%, 76.49%, and 86.75%. This improvement has the advantage that it does not add time delays and does not increases the overall cost of the rotary encoder. These results showed the real dimension of this structural misalignment problem and the great improvement in precision that can be achieved. Full article
(This article belongs to the Special Issue Sensors for Robotic Applications)
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Article
Improved Data Association of Hypothesis-Based Trackers Using Fast and Robust Object Initialization
Sensors 2021, 21(9), 3146; https://doi.org/10.3390/s21093146 - 01 May 2021
Viewed by 595
Abstract
The tracking of Vulnerable Road Users (VRU) is one of the vital tasks of autonomous cars. This includes estimating the positions and velocities of VRUs surrounding a car. To do this, VRU trackers must utilize measurements that are received from sensors. However, even [...] Read more.
The tracking of Vulnerable Road Users (VRU) is one of the vital tasks of autonomous cars. This includes estimating the positions and velocities of VRUs surrounding a car. To do this, VRU trackers must utilize measurements that are received from sensors. However, even the most accurate VRU trackers are affected by measurement noise, background clutter, and VRUs’ interaction and occlusion. Such uncertainties can cause deviations in sensors’ data association, thereby leading to dangerous situations and potentially even the failure of a tracker. The initialization of a data association depends on various parameters. This paper proposes steps to reveal the trade-offs between stochastic model parameters to improve data association’s accuracy in autonomous cars. The proposed steps can reduce the number of false tracks; besides, it is independent of variations in measurement noise and the number of VRUs. Our initialization can reduce the lag between the first detection and initialization of the VRU trackers. As a proof of concept, the procedure is validated using experiments, simulation data, and the publicly available KITTI dataset. Moreover, we compared our initialization method with the most popular approaches that were found in the literature. The results showed that the tracking precision and accuracy increase to 3.6% with the proposed initialization as compared to the state-of-the-art algorithms in tracking VRU. Full article
(This article belongs to the Special Issue Sensors for Robotic Applications)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: The implementation and evaluation of individual preference in robot facial expression based on emotion estimation using biological signals
Authors: Peeraya Sripian, Muhammad Nur Adilin Mohd Anuardi, Jiawei Yu, and Midori Sugaya
Affiliation: Shibaura Institute of Technology, Tokyo, Japan
Abstract: Recently, robot services have been widely applied to many fields. To provide optimum service, it is essential to maintain a good impression of the robot. Similar to human-human communication, if a robot could interpret human emotion and display some expression in responding to such emotion could lead to more effective interaction with users. Our previous works attempt to implement the expression by synchronizing the estimated human’s emotion onto the robot facial expression. For instance, the robot would show a sad face when it senses that the user was feeling sad. However, the latter experiment result revealed that it could pose different perceptions according to individual preferences. In this study, we focus on improving the robot’s impression by changing the robot’s expression according to the emotion of its interacting partner by considering individual preferences. The emotion is estimated using biological information. First, we evaluated the individual differences in the robot impression. The robot changes the expression according to three conditions; synchronized with the estimated emotion, inversely synchronized, and funny expression. During the experiment, the participants would give feedback toward the robot’s expression by choosing whether they “like” or “dislike” the expression. We investigated individual differences in the impression of robot expression using the SD method as subjective evaluation. At the same time, logistic regression is used to create a classification model by considering individual differences based on biological data and feedback from each participant. From the analysis, we found that the robot expression from inverse synchronization when the participants feel negative emotion could result in impression differences among individual. Then, the robot’s expression is determined based on the classification model created, and the SD method on the impression of the robot was compared with the three conditions. Overall, we found that the participants favored the most when the robot expression was determined based on individual differences

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