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Special Issue "Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans"

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

Deadline for manuscript submissions: 31 May 2021.

Special Issue Editors

Prof. Dr. Javier Cuadrado
Website
Guest Editor
Laboratory of Mechanical Engineering, Department of Naval and Industrial Engineering, University of La Coruña, 15403 Ferrol, Spain
Interests: multibody system dynamics and applications to automotive, biomechanics, and machinery sectors
Prof. Dr. Miguel Ángel Naya Villaverde
Website
Guest Editor
Laboratory of Mechanical Engineering, Department of Naval and Industrial Engineering, University of La Coruña, 15403 Ferrol, Spain
Interests: multibody system dynamics, vehicle dynamics, and applications to the automotive sector
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The combination of physical sensors and computational models to provide additional information about system states, inputs, and/or parameters, in what is known as virtual sensoring, is becoming more and more popular in many sectors, such as the automotive, aeronautics, aerospatial, railway, machinery, robotics, and human biomechanics sectors. While, in many cases, control-oriented models, which are generally simple, are the best choice, multibody models, which can be much more detailed, may have application, for example during the design stage of a new product.

This Special Issue seeks works dealing with the many challenges that must be overcome when developing multibody-based virtual sensors. These challenges include the selection of the fusion algorithm and its parameters, the coupling or independence between the fusion algorithm and the multibody formulation, magnitudes to be estimated, the stability and accuracy of the adopted solution, optimization of the computational cost, real-time issues, and implementation on embedded hardware. We also welcome studies on the application of multibody-based virtual sensors to, for example, vehicles, heavy machinery, mobile or humanoid robots, assistive orthotic and prosthetic devices, or the measurement and analysis of human movement.

Prof. Dr. Javier Cuadrado
Prof. Dr. Miguel Ángel Naya Villaverde
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 2200 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

  • multibody dynamics
  • virtual sensors
  • fusion algorithms
  • nonlinear filtering
  • kalman filtering
  • estimation/observation of states, inputs, and/or parameters
  • real-time applications
  • embedded hardware
  • heterogeneous computing

Published Papers (2 papers)

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Research

Open AccessArticle
A Track Geometry Measuring System Based on Multibody Kinematics, Inertial Sensors and Computer Vision
Sensors 2021, 21(3), 683; https://doi.org/10.3390/s21030683 - 20 Jan 2021
Abstract
This paper describes the kinematics used for the calculation of track geometric irregularities of a new Track Geometry Measuring System (TGMS) to be installed in railway vehicles. The TGMS includes a computer for data acquisition and process, a set of sensors including an [...] Read more.
This paper describes the kinematics used for the calculation of track geometric irregularities of a new Track Geometry Measuring System (TGMS) to be installed in railway vehicles. The TGMS includes a computer for data acquisition and process, a set of sensors including an inertial measuring unit (IMU, 3D gyroscope and 3D accelerometer), two video cameras and an encoder. The kinematic description, that is borrowed from the multibody dynamics analysis of railway vehicles used in computer simulation codes, is used to calculate the relative motion between the vehicle and the track, and also for the computer vision system and its calibration. The multibody framework is thus used to find the formulas that are needed to calculate the track irregularities (gauge, cross-level, alignment and vertical profile) as a function of sensor data. The TGMS has been experimentally tested in a 1:10 scaled vehicle and track specifically designed for this investigation. The geometric irregularities of a 90 m-scale track have been measured with an alternative and accurate method and the results are compared with the results of the TGMS. Results show a good agreement between both methods of calculation of the geometric irregularities. Full article
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Open AccessArticle
Using Accelerometer Data to Tune the Parameters of an Extended Kalman Filter for Optical Motion Capture: Preliminary Application to Gait Analysis
Sensors 2021, 21(2), 427; https://doi.org/10.3390/s21020427 - 09 Jan 2021
Abstract
Optical motion capture is currently the most popular method for acquiring motion data in biomechanical applications. However, it presents a number of problems that make the process difficult and inefficient, such as marker occlusions and unwanted reflections. In addition, the obtained trajectories must [...] Read more.
Optical motion capture is currently the most popular method for acquiring motion data in biomechanical applications. However, it presents a number of problems that make the process difficult and inefficient, such as marker occlusions and unwanted reflections. In addition, the obtained trajectories must be numerically differentiated twice in time in order to get the accelerations. Since the trajectories are normally noisy, they need to be filtered first, and the selection of the optimal amount of filtering is not trivial. In this work, an extended Kalman filter (EKF) that manages marker occlusions and undesired reflections in a robust way is presented. A preliminary test with inertial measurement units (IMUs) is carried out to determine their local reference frames. Then, the gait analysis of a healthy subject is performed using optical markers and IMUs simultaneously. The filtering parameters used in the optical motion capture process are tuned in order to achieve good correlation between the obtained accelerations and those measured by the IMUs. The results show that the EKF provides a robust and efficient method for optical system-based motion analysis, and that the availability of accelerations measured by inertial sensors can be very helpful for the adjustment of the filters. Full article
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