Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Sensing for Social Robots
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Sensing for Social Robots

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 8222

Special Issue Editors

Dr. João Silva Sequeira

E-Mail Website
Guest Editor
Instituto Superior Técnico, Lisbon University, 1049-001 Lisboa, Portugal
Interests: social robotics; human–robot interaction; architectural aspects of robotics, including robot modeling and control; networked and cooperative robotics; system integration
Dr. Rodrigo Ventura

E-Mail Website
Guest Editor
Instituto Superior Técnico, Lisbon University, 1049-001 Lisboa, Portugal
Interests: space robotics; collaborative robots; human-robot interaction; mobile manipulation; machine learning

Special Issue Information

Dear colleagues,

Social robots are getting ubiquitous, posing growing pressure on the robotics community to deliver these artificial entities able to play interesting roles in society. Robot skills are, slowly but surely, approaching humans’, i.e., to mimic and further extend humans’ typical skills. Hence, in addition to sensing matching human senses, there is a drive to develop novel sensors and processing techniques for the data produced.

The challenges for the sensors community are huge, ranging from materials to operation principles, mathematical models, sensor fusion, perception, and artificial intelligence, as well as hardware and software aspects. The need to estimate both concepts related to social sciences, e.g., emotions, and well-defined indicators, e.g., touch position and pressure, odors, and localization, is representative of the variety of sensor fields involved. In addition, networked social robotics extends the possibilities of sensor architectures. The dependability of robotics systems is affected by uncertainties typically present in social scenarios. Sensor uncertainties may even remain in the background of those intrinsic to social concepts, not affecting prospective robot skills. The importance of sensing in the context of social robots is thus clear.

This Special Issue aims at highlighting the current panorama and gathering novel ideas emerging in this exciting field.

Dr. João Silva Sequeira
Dr. Rodrigo Ventura
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.

Keywords

  • social robots
  • sensor uncertainties
  • perception
  • sensor fusion
  • sensor modeling
  • sensor networking
  • human senses
  • sensor dependability

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

33 pages, 7730 KiB  
Article
From Perception to Navigation in Environments with Persons: An Indoor Evaluation of the State of the Art
by Carlos Medina Sánchez, Matteo Zella, Jesús Capitán and Pedro J. Marrón
Sensors 2022, 22(3), 1191; https://doi.org/10.3390/s22031191 - 04 Feb 2022
Cited by 5 | Viewed by 3542
Abstract
Research in the field of social robotics is allowing service robots to operate in environments with people. In the aim of realizing the vision of humans and robots coexisting in the same environment, several solutions have been proposed to (1) perceive persons and [...] Read more.
Research in the field of social robotics is allowing service robots to operate in environments with people. In the aim of realizing the vision of humans and robots coexisting in the same environment, several solutions have been proposed to (1) perceive persons and objects in the immediate environment; (2) predict the movements of humans; as well as (3) plan the navigation in agreement with socially accepted rules. In this work, we discuss the different aspects related to social navigation in the context of our experience in an indoor environment. We describe state-of-the-art approaches and experiment with existing methods to analyze their performance in practice. From this study, we gather first-hand insights into the limitations of current solutions and identify possible research directions to address the open challenges. In particular, this paper focuses on topics related to perception at the hardware and application levels, including 2D and 3D sensors, geometric and mainly semantic mapping, the prediction of people trajectories (physics-, pattern- and planning-based), and social navigation (reactive and predictive) in indoor environments. Full article
(This article belongs to the Special Issue Sensing for Social Robots)
►▼ Show Figures

Figure 1

25 pages, 1863 KiB  
Article
A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States
by Maria J. Pinto-Bernal, Carlos A. Cifuentes, Oscar Perdomo, Monica Rincón-Roncancio and Marcela Múnera
Sensors 2021, 21(19), 6401; https://doi.org/10.3390/s21196401 - 25 Sep 2021
Cited by 11 | Viewed by 3445
Abstract
Physical exercise contributes to the success of rehabilitation programs and rehabilitation processes assisted through social robots. However, the amount and intensity of exercise needed to obtain positive results are unknown. Several considerations must be kept in mind for its implementation in rehabilitation, as [...] Read more.
Physical exercise contributes to the success of rehabilitation programs and rehabilitation processes assisted through social robots. However, the amount and intensity of exercise needed to obtain positive results are unknown. Several considerations must be kept in mind for its implementation in rehabilitation, as monitoring of patients’ intensity, which is essential to avoid extreme fatigue conditions, may cause physical and physiological complications. The use of machine learning models has been implemented in fatigue management, but is limited in practice due to the lack of understanding of how an individual’s performance deteriorates with fatigue; this can vary based on physical exercise, environment, and the individual’s characteristics. As a first step, this paper lays the foundation for a data analytic approach to managing fatigue in walking tasks. The proposed framework establishes the criteria for a feature and machine learning algorithm selection for fatigue management, classifying four fatigue diagnoses states. Based on the proposed framework and the classifier implemented, the random forest model presented the best performance with an average accuracy of ≥98% and F-score of ≥93%. This model was comprised of ≤16 features. In addition, the prediction performance was analyzed by limiting the sensors used from four IMUs to two or even one IMU with an overall performance of ≥88%. Full article
(This article belongs to the Special Issue Sensing for Social Robots)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop