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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: closed (31 August 2021) | Viewed by 31343

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Javier Cuadrado

E-Mail 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 A. Naya

E-Mail Website
Guest Editor
Department of Naval and Industrial Engineering, University of La Coruña, 15403 Ferrol, Spain
Interests: multibody system dynamics; vehicle dynamics; applications to the automotive sector
Special Issues, Collections and Topics 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 A. Naya
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

  • 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 (11 papers)

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Editorial

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2 pages, 185 KiB  
Editorial
Editorial of Special Issue “Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans”
by Javier Cuadrado and Miguel Á. Naya
Sensors 2021, 21(19), 6345; https://0-doi-org.brum.beds.ac.uk/10.3390/s21196345 - 23 Sep 2021
Cited by 2 | Viewed by 1368
Abstract
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 sensing, is becoming more and more popular in many sectors, such as the automotive, aeronautics, aerospatial, railway, machinery, robotics [...] Read more.
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 sensing, is becoming more and more popular in many sectors, such as the automotive, aeronautics, aerospatial, railway, machinery, robotics and human biomechanics sectors [...] Full article
(This article belongs to the Special Issue Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans)

Research

Jump to: Editorial

25 pages, 95763 KiB  
Article
Lunar Surface Fault-Tolerant Soft-Landing Performance and Experiment for a Six-Legged Movable Repetitive Lander
by Ke Yin, Songlin Zhou, Qiao Sun and Feng Gao
Sensors 2021, 21(17), 5680; https://0-doi-org.brum.beds.ac.uk/10.3390/s21175680 - 24 Aug 2021
Cited by 3 | Viewed by 2537
Abstract
The cascading launch and cooperative work of lander and rover are the pivotal methods to achieve lunar zero-distance exploration. The separated design results in a heavy system mass that requires more launching costs and a limited exploration area that is restricted to the [...] Read more.
The cascading launch and cooperative work of lander and rover are the pivotal methods to achieve lunar zero-distance exploration. The separated design results in a heavy system mass that requires more launching costs and a limited exploration area that is restricted to the vicinity of the immovable lander. To solve this problem, we have designed a six-legged movable repetitive lander, called “HexaMRL”, which congenitally integrates the function of both the lander and rover. However, achieving a buffered landing after a failure of the integrated drive units (IDUs) in the harsh lunar environment is a great challenge. In this paper, we systematically analyze the fault-tolerant capacity of all possible landing configurations in which the number of remaining normal legs is more than two and design the landing algorithm to finish a fault-tolerant soft-landing for the stable configuration. A quasi-incentre stability optimization method is further proposed to increase the stability margin during supporting operations after landing. To verify the fault-tolerant landing performance on the moon, a series of experiments, including five-legged, four-legged and three-legged soft-landings with a vertical landing velocity of −1.9 m/s and a payload of 140 kg, are successfully carried out on a 5-DoF lunar gravity ground-testing platform. The HexaMRL with fault-tolerant landing capacity will greatly promote the development of a next-generation lunar prober. Full article
(This article belongs to the Special Issue Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans)
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16 pages, 2275 KiB  
Article
A Factor-Graph-Based Approach to Vehicle Sideslip Angle Estimation
by Antonio Leanza, Giulio Reina and José-Luis Blanco-Claraco
Sensors 2021, 21(16), 5409; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165409 - 10 Aug 2021
Cited by 6 | Viewed by 2358
Abstract
Sideslip angle is an important variable for understanding and monitoring vehicle dynamics, but there is currently no inexpensive method for its direct measurement. Therefore, it is typically estimated from proprioceptive sensors onboard using filtering methods from the family of the Kalman filter. As [...] Read more.
Sideslip angle is an important variable for understanding and monitoring vehicle dynamics, but there is currently no inexpensive method for its direct measurement. Therefore, it is typically estimated from proprioceptive sensors onboard using filtering methods from the family of the Kalman filter. As a novel alternative, this work proposes modeling the problem directly as a graphical model (factor graph), which can then be optimized using a variety of methods, such as whole-dataset batch optimization for offline processing or fixed-lag smoothing for on-line operation. Experimental results on real vehicle datasets validate the proposal, demonstrating a good agreement between estimated and actual sideslip angle, showing similar performance to state-of-the-art methods but with a greater potential for future extensions due to the more flexible mathematical framework. An open-source implementation of the proposed framework has been made available online. Full article
(This article belongs to the Special Issue Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans)
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24 pages, 1542 KiB  
Article
Multibody-Based Input and State Observers Using Adaptive Extended Kalman Filter
by Antonio J. Rodríguez, Emilio Sanjurjo, Roland Pastorino and Miguel Á. Naya
Sensors 2021, 21(15), 5241; https://0-doi-org.brum.beds.ac.uk/10.3390/s21155241 - 03 Aug 2021
Cited by 4 | Viewed by 2329
Abstract
The aim of this work is to explore the suitability of adaptive methods for state estimators based on multibody dynamics, which present severe non-linearities. The performance of a Kalman filter relies on the knowledge of the noise covariance matrices, which are difficult to [...] Read more.
The aim of this work is to explore the suitability of adaptive methods for state estimators based on multibody dynamics, which present severe non-linearities. The performance of a Kalman filter relies on the knowledge of the noise covariance matrices, which are difficult to obtain. This challenge can be overcome by the use of adaptive techniques. Based on an error-extended Kalman filter with force estimation (errorEKF-FE), the adaptive method known as maximum likelihood is adjusted to fulfill the multibody requirements. This new filter is called adaptive error-extended Kalman filter (AerrorEKF-FE). In order to present a general approach, the method is tested on two different mechanisms in a simulation environment. In addition, different sensor configurations are also studied. Results show that, in spite of the maneuver conditions and initial statistics, the AerrorEKF-FE provides estimations with accuracy and robustness. The AerrorEKF-FE proves that adaptive techniques can be applied to multibody-based state estimators, increasing, therefore, their fields of application. Full article
(This article belongs to the Special Issue Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans)
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23 pages, 1860 KiB  
Article
Estimating the Characteristic Curve of a Directional Control Valve in a Combined Multibody and Hydraulic System Using an Augmented Discrete Extended Kalman Filter
by Qasim Khadim, Mehran Kiani-Oshtorjani, Suraj Jaiswal, Marko K. Matikainen and Aki Mikkola
Sensors 2021, 21(15), 5029; https://0-doi-org.brum.beds.ac.uk/10.3390/s21155029 - 24 Jul 2021
Cited by 12 | Viewed by 2489
Abstract
The estimation of the parameters of a simulation model such that the model’s behaviour matches closely with reality can be a cumbersome task. This is due to the fact that a number of model parameters cannot be directly measured, and such parameters might [...] Read more.
The estimation of the parameters of a simulation model such that the model’s behaviour matches closely with reality can be a cumbersome task. This is due to the fact that a number of model parameters cannot be directly measured, and such parameters might change during the course of operation in a real system. Friction between different machine components is one example of these parameters. This can be due to a number of reasons, such as wear. Nevertheless, if one is able to accurately define all necessary parameters, essential information about the performance of the system machinery can be acquired. This information can be, in turn, utilised for product-specific tuning or predictive maintenance. To estimate parameters, the augmented discrete extended Kalman filter with a curve fitting method can be used, as demonstrated in this paper. In this study, the proposed estimation algorithm is applied to estimate the characteristic curves of a directional control valve in a four-bar mechanism actuated by a fluid power system. The mechanism is modelled by using the double-step semi-recursive multibody formulation, whereas the fluid power system under study is modelled by employing the lumped fluid theory. In practise, the characteristic curves of a directional control valve is described by three to six data control points of a third-order B-spline curve in the augmented discrete extended Kalman filter. The results demonstrate that the highly non-linear unknown characteristic curves can be estimated by using the proposed parameter estimation algorithm. It is also demonstrated that the root mean square error associated with the estimation of the characteristic curve is 0.08% with respect to the real model. In addition, all the errors in the estimated states and parameters of the system are within the 95% confidence interval. The estimation of the characteristic curve in a hydraulic valve can provide essential information for performance monitoring and maintenance applications. Full article
(This article belongs to the Special Issue Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans)
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32 pages, 13957 KiB  
Article
Haptic Devices Based on Real-Time Dynamic Models of Multibody Systems
by Nicolas Docquier, Sébastien Timmermans and Paul Fisette
Sensors 2021, 21(14), 4794; https://0-doi-org.brum.beds.ac.uk/10.3390/s21144794 - 14 Jul 2021
Cited by 4 | Viewed by 2507
Abstract
Multibody modeling of mechanical systems can be applied to various applications. Human-in-the-loop interfaces represent a growing research field, for which increasingly more devices include a dynamic multibody model to emulate the system physics in real-time. In this scope, reliable and highly dynamic sensors, [...] Read more.
Multibody modeling of mechanical systems can be applied to various applications. Human-in-the-loop interfaces represent a growing research field, for which increasingly more devices include a dynamic multibody model to emulate the system physics in real-time. In this scope, reliable and highly dynamic sensors, to both validate those models and to measure in real-time the physical system behavior, have become crucial. In this paper, a multibody modeling approach in relative coordinates is proposed, based on symbolic equations of the physical system. The model is running in a ROS environment, which interacts with sensors and actuators. Two real-time applications with haptic feedback are presented: a piano key and a car simulator. In the present work, several sensors are used to characterize and validate the multibody model, but also to measure the system kinematics and dynamics within the human-in-the-loop process, and to ultimately validate the haptic device behavior. Experimental results for both developed devices confirm the interest of an embedded multibody model to enhance the haptic feedback performances. Besides, model parameters variations during the experiments illustrate the infinite possibilities that such model-based configurable haptic devices can offer. Full article
(This article belongs to the Special Issue Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans)
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24 pages, 4090 KiB  
Article
Virtual Sensoring of Motion Using Pontryagin’s Treatment of Hamiltonian Systems
by Timothy Sands
Sensors 2021, 21(13), 4603; https://0-doi-org.brum.beds.ac.uk/10.3390/s21134603 - 05 Jul 2021
Cited by 22 | Viewed by 2868
Abstract
To aid the development of future unmanned naval vessels, this manuscript investigates algorithm options for combining physical (noisy) sensors and computational models to provide additional information about system states, inputs, and parameters emphasizing deterministic options rather than stochastic ones. The computational model is [...] Read more.
To aid the development of future unmanned naval vessels, this manuscript investigates algorithm options for combining physical (noisy) sensors and computational models to provide additional information about system states, inputs, and parameters emphasizing deterministic options rather than stochastic ones. The computational model is formulated using Pontryagin’s treatment of Hamiltonian systems resulting in optimal and near-optimal results dependent upon the algorithm option chosen. Feedback is proposed to re-initialize the initial values of a reformulated two-point boundary value problem rather than using state feedback to form errors that are corrected by tuned estimators. Four algorithm options are proposed with two optional branches, and all of these are compared to three manifestations of classical estimation methods including linear-quadratic optimal. Over ten-thousand simulations were run to evaluate each proposed method’s vulnerability to variations in plant parameters amidst typically noisy state and rate sensors. The proposed methods achieved 69–72% improved state estimation, 29–33% improved rate improvement, while simultaneously achieving mathematically minimal costs of utilization in guidance, navigation, and control decision criteria. The next stage of research is indicated throughout the manuscript: investigation of the proposed methods’ efficacy amidst unknown wave disturbances. Full article
(This article belongs to the Special Issue Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans)
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24 pages, 2988 KiB  
Article
A Discrete-Time Extended Kalman Filter Approach Tailored for Multibody Models: State-Input Estimation
by Rocco Adduci, Martijn Vermaut, Frank Naets, Jan Croes and Wim Desmet
Sensors 2021, 21(13), 4495; https://0-doi-org.brum.beds.ac.uk/10.3390/s21134495 - 30 Jun 2021
Cited by 9 | Viewed by 2639
Abstract
Model-based force estimation is an emerging methodology in the mechatronic community given the possibility to exploit physically inspired high-fidelity models in tandem with ready-to-use cheap sensors. In this work, an inverse input load identification methodology is presented combining high-fidelity multibody models with a [...] Read more.
Model-based force estimation is an emerging methodology in the mechatronic community given the possibility to exploit physically inspired high-fidelity models in tandem with ready-to-use cheap sensors. In this work, an inverse input load identification methodology is presented combining high-fidelity multibody models with a Kalman filter-based estimator and providing the means for an accurate and computationally efficient state-input estimation strategy. A particular challenge addressed in this work is the handling of the redundant state-description encountered in common multibody model descriptions. A novel linearization framework is proposed on the time-discretized equations in order to extract the required system model matrices for the Kalman filter. The presented framework is experimentally validated on a slider-crank mechanism. The nonlinear kinematics and dynamics are well represented through a rigid multibody model with lumped flexibilities to account for localized interaction phenomena among bodies. The proposed methodology is validated estimating the input torque delivered by a driver electro-motor together with the system states and comparing the experimental data with the estimated quantities. The results show the stability and accuracy of the estimation framework by only employing the angular motor velocity, measured by the motor encoder sensor and available in most of the commercial electro-motors. Full article
(This article belongs to the Special Issue Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans)
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22 pages, 53351 KiB  
Article
Vision-Based Hybrid Controller to Release a 4-DOF Parallel Robot from a Type II Singularity
by José L. Pulloquinga, Rafael J. Escarabajal, Jesús Ferrándiz, Marina Vallés, Vicente Mata and Mónica Urízar
Sensors 2021, 21(12), 4080; https://0-doi-org.brum.beds.ac.uk/10.3390/s21124080 - 13 Jun 2021
Cited by 9 | Viewed by 2936
Abstract
The high accuracy and dynamic performance of parallel robots (PRs) make them suitable to ensure safe operation in human–robot interaction. However, these advantages come at the expense of a reduced workspace and the possible appearance of type II singularities. The latter is due [...] Read more.
The high accuracy and dynamic performance of parallel robots (PRs) make them suitable to ensure safe operation in human–robot interaction. However, these advantages come at the expense of a reduced workspace and the possible appearance of type II singularities. The latter is due to the loss of control of the PR and requires further analysis to keep the stiffness of the PR even after a singular configuration is reached. All or a subset of the limbs could be responsible for a type II singularity, and they can be detected by using the angle between two output twist screws (OTSs). However, this angle has not been applied in control because it requires an accurate measure of the pose of the PR. This paper proposes a new hybrid controller to release a 4-DOF PR from a type II singularity based on a real time vision system. The vision system data are used to automatically readapt the configuration of the PR by moving the limbs identified by the angle between two OTSs. This controller is intended for a knee rehabilitation PR, and the results show how this release is accomplished with smooth controlled movements where the patient’s safety is not compromised. Full article
(This article belongs to the Special Issue Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans)
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27 pages, 18605 KiB  
Article
A Track Geometry Measuring System Based on Multibody Kinematics, Inertial Sensors and Computer Vision
by José L. Escalona, Pedro Urda and Sergio Muñoz
Sensors 2021, 21(3), 683; https://0-doi-org.brum.beds.ac.uk/10.3390/s21030683 - 20 Jan 2021
Cited by 17 | Viewed by 3431
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
(This article belongs to the Special Issue Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans)
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22 pages, 5487 KiB  
Article
Using Accelerometer Data to Tune the Parameters of an Extended Kalman Filter for Optical Motion Capture: Preliminary Application to Gait Analysis
by Javier Cuadrado, Florian Michaud, Urbano Lugrís and Manuel Pérez Soto
Sensors 2021, 21(2), 427; https://0-doi-org.brum.beds.ac.uk/10.3390/s21020427 - 09 Jan 2021
Cited by 17 | Viewed by 3874
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
(This article belongs to the Special Issue Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans)
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