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Sensors for Navigation and Control Systems

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

Deadline for manuscript submissions: closed (20 November 2023) | Viewed by 32157

Special Issue Editor

Prof. Dr. Antonios Tsourdos

E-Mail Website
Guest Editor
Centre for Autonomous and Cyberphysical Systems, Cranfield University, Cranfield MK43 0AL, UK
Interests: unmanned aircraft systems; decision making on multi-agent systems; data-centric guidance and control; swarm
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue to highlight the most recent research regarding to sensors for navigation and control systems. This Special Issue focuses on the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of sensors for navigation and control systems. Research articles and reviews are solicited that provide a comprehensive insight into the sensor technologies for navigation and control systems on any aspect of novel sensor development and applications. Topics of interest include but are not limited to the following:

  • control sensors and advanced applications
  • navigation and positioning
  • sensor technology in applications of control engineering
  • accelerometers, inclinometers and gyroscopes
  • multi sensor fusion technology
  • smart and intelligent sensors
  • sensor interfacing and signal conditioning
  • sensor calibration    
  • data fusion and deep learning in sensor systems
  • localization and object tracking
  • path planning
  • motion planning
  • adaptive guidance and control
  • vison-based navigation
  • fault tolerant control

Prof. Dr. Antonios Tsourdos
Guest Editor

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.

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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.

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Published Papers (16 papers)

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Research

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18 pages, 5768 KiB  
Article
Reducing the Uncertainty of the Moving Object Location Measurement with the Method of Quasi-Multiple Measurement in GNSS Technology in Symmetrical Arrangement
by Jacek Skibicki, Andrzej Wilk, Władysław Koc, Roksana Licow, Jacek Szmagliński, Piotr Chrostowski, Slawomir Judek, Krzysztof Karwowski and Sławomir Grulkowski
Sensors 2023, 23(5), 2657; https://doi.org/10.3390/s23052657 - 28 Feb 2023
Viewed by 1111
Abstract
The article presents a solution to the problem of limited accuracy of dynamic measurements performed with GNSS receivers. The proposed measurement method is a response to the needs related to the assessment of the measurement uncertainty of the position of the track axis [...] Read more.
The article presents a solution to the problem of limited accuracy of dynamic measurements performed with GNSS receivers. The proposed measurement method is a response to the needs related to the assessment of the measurement uncertainty of the position of the track axis of the rail transport line. However, the problem of reducing the measurement uncertainty is universal for many different situations where high accuracy of positioning of objects is required, especially in motion. The article proposes a new method to determine object’s location using geometric constraints of a number of GNSS receivers arranged in symmetric configuration. The proposed method has been verified by comparing signals recorded by up to five GNSS receivers during stationary and dynamic measurements. The dynamic measurement was made on a tram track within the framework of a cycle of studies upon effective and efficient methods to catalogue and diagnose tracks. A detailed analysis of the results obtained with the quasi-multiple measurement method confirms remarkable reduction in their uncertainty. Their synthesis shows the usability of this method in dynamic conditions. The proposed method is expected to find application in measurements requiring high accuracy, and in case of deterioration of the signal quality from satellites by one or more of GNSS receivers due to the appearance of natural obstacles. Full article
(This article belongs to the Special Issue Sensors for Navigation and Control Systems)
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23 pages, 2681 KiB  
Article
Utilization of Carrier-Frequency Offset Measurements in UWB TDoA Positioning with Receiving Tag
by Josef Krška and Václav Navrátil
Sensors 2023, 23(5), 2595; https://doi.org/10.3390/s23052595 - 26 Feb 2023
Cited by 5 | Viewed by 1685
Abstract
High-capacity impulse-radio ultra-wideband (IR-UWB) indoor localization systems are typically based on the time difference of arrival (TDoA) principle. When the fixed and synchronized localization infrastructure, the anchors, transmit precisely timestamped messages, a virtually unlimited number of user receivers (tags) are able to estimate [...] Read more.
High-capacity impulse-radio ultra-wideband (IR-UWB) indoor localization systems are typically based on the time difference of arrival (TDoA) principle. When the fixed and synchronized localization infrastructure, the anchors, transmit precisely timestamped messages, a virtually unlimited number of user receivers (tags) are able to estimate their position based on differences in the time of arrival of those messages. However, the drift of the tag clock causes systematic errors at a sufficiently high magnitude to effectively deny the positioning, if left uncorrected. Previously, the extended Kalman filter (EKF) has been used to track and compensate for the clock drift. In this article, the utilization of a carrier frequency offset (CFO) measurement for suppressing the clock-drift related error in anchor-to-tag positioning is presented and compared to the filtered solution. The CFO is readily available in the coherent UWB transceivers, such as Decawave DW1000. It is inherently related to the clock drift, since both carrier and timestamping frequencies are derived from the identical reference oscillator. The experimental evaluation shows that the CFO-aided solution performs worse than the EKF-based solution in terms of accuracy. Nonetheless, with CFO-aiding it is possible to obtain a solution based on measurements from a single epoch, which is favorable especially for power-constrained applications. Full article
(This article belongs to the Special Issue Sensors for Navigation and Control Systems)
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27 pages, 3805 KiB  
Article
Assessment of GNSS Galileo Contribution to the Modernization of CROPOS’s Services
by Danijel Šugar, Ana Kliman, Željko Bačić and Zvonimir Nevistić
Sensors 2023, 23(5), 2466; https://doi.org/10.3390/s23052466 - 23 Feb 2023
Cited by 1 | Viewed by 1783
Abstract
CROPOS, as the Croatian GNSS network, was modernized and upgraded to support the Galileo system in 2019. Two of CROPOS’s services—VPPS (Network RTK service) and GPPS (post-processing service)—were assessed for the contribution of the Galileo system to their performance. A station used for [...] Read more.
CROPOS, as the Croatian GNSS network, was modernized and upgraded to support the Galileo system in 2019. Two of CROPOS’s services—VPPS (Network RTK service) and GPPS (post-processing service)—were assessed for the contribution of the Galileo system to their performance. A station used for field testing was previously examined and surveyed to determine the local horizon and to carry out a detailed mission planning. The whole day of observation was divided into several sessions, each with a different visibility of Galileo satellites. A special observation sequence was designed: VPPS (GPS-GLO-GAL), VPPS (GAL-only), and GPPS (GPS-GLO-GAL-BDS). All observations were taken on the same station with the same GNSS receiver, Trimble R12. Each static observation session was post-processed in Trimble Business Center (TBC) in two different ways: considering all available systems (GGGB) and considering GAL-only observations. A daily static solution based on all systems (GGGB) was considered as the reference for the accuracy assessment of all obtained solutions. The results obtained with VPPS (GPS-GLO-GAL) and VPPS (GAL-only) were analyzed and assessed; the results obtained with GAL-only have shown a slightly higher scatter. It was concluded that the inclusion of the Galileo system in CROPOS has contributed to the availability and reliability of solutions but not to their accuracy. By complying with the observation rules and taking redundant measurements, the accuracy of GAL-only results can be improved. Full article
(This article belongs to the Special Issue Sensors for Navigation and Control Systems)
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12 pages, 5537 KiB  
Article
Application of Sensors for Incorrect Behavior Identification in a Transport System
by Martin Mantič, Jozef Kuľka, Robert Grega, Matúš Virostko and Melichar Kopas
Sensors 2023, 23(3), 1635; https://doi.org/10.3390/s23031635 - 02 Feb 2023
Cited by 1 | Viewed by 1307
Abstract
This article focuses on cranes that are moving on a fixed crane track. There are specific problems and malfunctions arising during the operation of these cranes caused mainly by the crane skewing phenomenon. Crane skewing induces undesirable additional forces as a result of [...] Read more.
This article focuses on cranes that are moving on a fixed crane track. There are specific problems and malfunctions arising during the operation of these cranes caused mainly by the crane skewing phenomenon. Crane skewing induces undesirable additional forces as a result of force contact between the crane wheel flange and the head of the crane track rail. This negative phenomenon induces additional stress in the crane construction as well as wear of the crane components and, finally, a global reduction of the crane operational durability and reliability. There is described in this article a methodology and data processing for the experimental measurement targeted on the crane skewing, namely in the case of a bridge crane installed in a laboratory. The crane skewing phenomenon was experimentally induced by an intentional disruption of speed synchronization of the crane travel drives. The intensity of the crane skewing was measured by means of the strain gauge sensors. A suitable application of the measuring sensors, together with the utilization of the drive control algorithm, enables efficiency to eliminate crane skewing and also prevent its occurrence. Full article
(This article belongs to the Special Issue Sensors for Navigation and Control Systems)
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17 pages, 9153 KiB  
Article
Vision System Measuring the Position of an Aircraft in Relation to the Runway during Landing Approach
by Damian Kordos, Paweł Krzaczkowski, Paweł Rzucidło, Zbigniew Gomółka, Ewa Zesławska and Bogusław Twaróg
Sensors 2023, 23(3), 1560; https://doi.org/10.3390/s23031560 - 01 Feb 2023
Cited by 1 | Viewed by 2044
Abstract
This paper presents a vision system that measures the position of an aircraft relative to the runway (RWY) during a landing approach. It was assumed that all the information necessary for a correct approach was based entirely on an analysis of the image [...] Read more.
This paper presents a vision system that measures the position of an aircraft relative to the runway (RWY) during a landing approach. It was assumed that all the information necessary for a correct approach was based entirely on an analysis of the image of the runway and its surroundings. It was assumed that the way the algorithm works, as well as possible, should imitate the pilot’s perception of the runway. Taking into account the above and the fact that the infrastructure at each airport is different, it has been decided to use artificial neural networks with a dedicated learning process for any airport, based on the simulation environments. Such an action will enable the generation of a synthetic video sequence without the need for costly and time-consuming flights. The presented solution was tested in real flight conditions on an experimental aircraft, and the selected test results are presented in this article. Full article
(This article belongs to the Special Issue Sensors for Navigation and Control Systems)
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24 pages, 8761 KiB  
Article
JUNO Project: Deployment and Validation of a Low-Cost Cloud-Based Robotic Platform for Reliable Smart Navigation and Natural Interaction with Humans in an Elderly Institution
by Nieves Pavón-Pulido, Jesús Damián Blasco-García, Juan Antonio López-Riquelme, Jorge Feliu-Batlle, Roberto Oterino-Bono and María Trinidad Herrero
Sensors 2023, 23(1), 483; https://doi.org/10.3390/s23010483 - 02 Jan 2023
Cited by 1 | Viewed by 1904
Abstract
This paper describes the main results of the JUNO project, a proof of concept developed in the Region of Murcia in Spain, where a smart assistant robot with capabilities for smart navigation and natural human interaction has been developed and deployed, and it [...] Read more.
This paper describes the main results of the JUNO project, a proof of concept developed in the Region of Murcia in Spain, where a smart assistant robot with capabilities for smart navigation and natural human interaction has been developed and deployed, and it is being validated in an elderly institution with real elderly users. The robot is focused on helping people carry out cognitive stimulation exercises and other entertainment activities since it can detect and recognize people, safely navigate through the residence, and acquire information about attention while users are doing the mentioned exercises. All the information could be shared through the Cloud, if needed, and health professionals, caregivers and relatives could access such information by considering the highest standards of privacy required in these environments. Several tests have been performed to validate the system, which combines classic techniques and new Deep Learning-based methods to carry out the requested tasks, including semantic navigation, face detection and recognition, speech to text and text to speech translation, and natural language processing, working both in a local and Cloud-based environment, obtaining an economically affordable system. The paper also discusses the limitations of the platform and proposes several solutions to the detected drawbacks in this kind of complex environment, where the fragility of users should be also considered. Full article
(This article belongs to the Special Issue Sensors for Navigation and Control Systems)
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28 pages, 13231 KiB  
Article
Generation of Correction Data for Autonomous Driving by Means of Machine Learning and On-Board Diagnostics
by Alberto Flores Fernández, Eduardo Sánchez Morales, Michael Botsch, Christian Facchi and Andrés García Higuera
Sensors 2023, 23(1), 159; https://doi.org/10.3390/s23010159 - 23 Dec 2022
Cited by 2 | Viewed by 2032
Abstract
A highly accurate reference vehicle state is a requisite for the evaluation and validation of Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADASs). This highly accurate vehicle state is usually obtained by means of Inertial Navigation Systems (INSs) that obtain position, velocity, [...] Read more.
A highly accurate reference vehicle state is a requisite for the evaluation and validation of Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADASs). This highly accurate vehicle state is usually obtained by means of Inertial Navigation Systems (INSs) that obtain position, velocity, and Course Over Ground (COG) correction data from Satellite Navigation (SatNav). However, SatNav is not always available, as is the case of roofed places, such as parking structures, tunnels, or urban canyons. This leads to a degradation over time of the estimated vehicle state. In the present paper, a methodology is proposed that consists on the use of a Machine Learning (ML)-method (Transformer Neural Network—TNN) with the objective of generating highly accurate velocity correction data from On-Board Diagnostics (OBD) data. The TNN obtains OBD data as input and measurements from state-of-the-art reference sensors as a learning target. The results show that the TNN is able to infer the velocity over ground with a Mean Absolute Error (MAE) of 0.167 kmh (0.046 ms) when a database of 3,428,099 OBD measurements is considered. The accuracy decreases to 0.863 kmh (0.24 ms) when only 5000 OBD measurements are used. Given that the obtained accuracy closely resembles that of state-of-the-art reference sensors, it allows INSs to be provided with accurate velocity correction data. An inference time of less than 40 ms for the generation of new correction data is achieved, which suggests the possibility of online implementation. This supports a highly accurate estimation of the vehicle state for the evaluation and validation of AD and ADAS, even in SatNav-deprived environments. Full article
(This article belongs to the Special Issue Sensors for Navigation and Control Systems)
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19 pages, 23155 KiB  
Article
Angular Misalignment Calibration for Dual-Antenna GNSS/IMU Navigation Sensor
by Alexander Kozlov and Fedor Kapralov
Sensors 2023, 23(1), 77; https://doi.org/10.3390/s23010077 - 21 Dec 2022
Cited by 1 | Viewed by 1746
Abstract
We address the angular misalignment calibration problem, which arises when a multi-antenna GNSS serves as a source of aiding information for inertial sensors in an integrated navigation system. Antennas usually occupy some outside structure of the moving carrier object, whilst an inertial measurement [...] Read more.
We address the angular misalignment calibration problem, which arises when a multi-antenna GNSS serves as a source of aiding information for inertial sensors in an integrated navigation system. Antennas usually occupy some outside structure of the moving carrier object, whilst an inertial measurement unit typically remains inside. Especially when using low- or mid-grade MEMS gyroscopes and accelerometers, it is either impossible or impractical to physically align IMU-sensitive axes and GNSS antenna baselines within some 1–3 degrees due to the micromechanical nature of the inertial sensors: they are just too small to have any physical reference features to align to. However, in some applications, it is desirable to line up all sensors within a fraction-of-a-degree level of accuracy. One may imagine solving this problem via the long-term averaging of sensor signals in different positions to ensure observability and then using angle differences for analytical compensation. We suggest faster calibration in special rotations using sensor fusion. Apart from quicker convergence, this method also accounts for run-to-run inertial sensor bias instability. In addition, it allows further on-the-fly finer calibration in the background when the navigation system performs its regular operation, and carrier objects may undergo gradual deformations of its structure over the years. Full article
(This article belongs to the Special Issue Sensors for Navigation and Control Systems)
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14 pages, 7439 KiB  
Article
A Benchmark Comparison of Four Off-the-Shelf Proprietary Visual–Inertial Odometry Systems
by Pyojin Kim, Jungha Kim, Minkyeong Song, Yeoeun Lee, Moonkyeong Jung and Hyeong-Geun Kim
Sensors 2022, 22(24), 9873; https://doi.org/10.3390/s22249873 - 15 Dec 2022
Cited by 4 | Viewed by 2369
Abstract
Commercial visual–inertial odometry (VIO) systems have been gaining attention as cost-effective, off-the-shelf, six-degree-of-freedom (6-DoF) ego-motion-tracking sensors for estimating accurate and consistent camera pose data, in addition to their ability to operate without external localization from motion capture or global positioning systems. It is [...] Read more.
Commercial visual–inertial odometry (VIO) systems have been gaining attention as cost-effective, off-the-shelf, six-degree-of-freedom (6-DoF) ego-motion-tracking sensors for estimating accurate and consistent camera pose data, in addition to their ability to operate without external localization from motion capture or global positioning systems. It is unclear from existing results, however, which commercial VIO platforms are the most stable, consistent, and accurate in terms of state estimation for indoor and outdoor robotic applications. We assessed four popular proprietary VIO systems (Apple ARKit, Google ARCore, Intel RealSense T265, and Stereolabs ZED 2) through a series of both indoor and outdoor experiments in which we showed their positioning stability, consistency, and accuracy. After evaluating four popular VIO sensors in challenging real-world indoor and outdoor scenarios, Apple ARKit showed the most stable and high accuracy/consistency, and the relative pose error was a drift error of about 0.02 m per second. We present our complete results as a benchmark comparison for the research community. Full article
(This article belongs to the Special Issue Sensors for Navigation and Control Systems)
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16 pages, 4866 KiB  
Article
Landmark-Based Scale Estimation and Correction of Visual Inertial Odometry for VTOL UAVs in a GPS-Denied Environment
by Jyun-Cheng Lee, Chih-Chun Chen, Chang-Te Shen and Ying-Chih Lai
Sensors 2022, 22(24), 9654; https://doi.org/10.3390/s22249654 - 09 Dec 2022
Cited by 6 | Viewed by 1928
Abstract
With the rapid development of technology, unmanned aerial vehicles (UAVs) have become more popular and are applied in many areas. However, there are some environments where the Global Positioning System (GPS) is unavailable or has the problem of GPS signal outages, such as [...] Read more.
With the rapid development of technology, unmanned aerial vehicles (UAVs) have become more popular and are applied in many areas. However, there are some environments where the Global Positioning System (GPS) is unavailable or has the problem of GPS signal outages, such as indoor and bridge inspections. Visual inertial odometry (VIO) is a popular research solution for non-GPS navigation. However, VIO has problems of scale errors and long-term drift. This study proposes a method to correct the position errors of VIO without the help of GPS information for vertical takeoff and landing (VTOL) UAVs. In the initial process, artificial landmarks are utilized to improve the positioning results of VIO by the known landmark information. The position of the UAV is estimated by VIO. Then, the accurate position is estimated by the extended Kalman filter (EKF) with the known landmark, which is used to obtain the scale correction using the least squares method. The Inertial Measurement Unit (IMU) data are used for integration in the time-update process. The EKF can be updated with two measurements. One is the visual odometry (VO) estimated directly by a landmark. The other is the VIO with scale correction. When the landmark is detected during takeoff phase, or the UAV is returning to the takeoff location during landing phase, the trajectory estimated by the landmark is used to update the scale correction. At the beginning of the experiments, preliminary verification was conducted on the ground. A self-developed UAV equipped with a visual–inertial sensor to collect data and a high-precision real time kinematic (RTK) to verify trajectory are applied to flight tests. The experimental results show that the method proposed in this research effectively solves the problems of scale and the long-term drift of VIO. Full article
(This article belongs to the Special Issue Sensors for Navigation and Control Systems)
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15 pages, 4031 KiB  
Article
Passive Sonar Target Identification Using Multiple-Measurement Sparse Bayesian Learning
by Myoungin Shin, Wooyoung Hong, Keunhwa Lee and Youngmin Choo
Sensors 2022, 22(21), 8511; https://doi.org/10.3390/s22218511 - 04 Nov 2022
Cited by 1 | Viewed by 1775
Abstract
Accurate estimation of the frequency component is an important issue to identify and track marine objects (e.g., surface ship, submarine, etc.). In general, a passive sonar system consists of a sensor array, and each sensor receives data that have common information of the [...] Read more.
Accurate estimation of the frequency component is an important issue to identify and track marine objects (e.g., surface ship, submarine, etc.). In general, a passive sonar system consists of a sensor array, and each sensor receives data that have common information of the target signal. In this paper, we consider multiple-measurement sparse Bayesian learning (MM-SBL), which reconstructs sparse solutions in a linear system using Bayesian frameworks, to detect the common frequency components received by each sensor. In addition, the direction of arrival estimation was performed on each detected common frequency component using the MM-SBL based on beamforming. The azimuth for each common frequency component was confirmed in the frequency-azimuth plot, through which we identified the target. In addition, we perform target tracking using the target detection results along time, which are derived from the sum of the signal spectrum at the azimuth angle. The performance of the MM-SBL and the conventional target detection method based on energy detection were compared using in-situ data measured near the Korean peninsula, where MM-SBL displays superior detection performance and high-resolution results. Full article
(This article belongs to the Special Issue Sensors for Navigation and Control Systems)
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14 pages, 3495 KiB  
Article
Learning Assurance Analysis for Further Certification Process of Machine Learning Techniques: Case-Study Air Traffic Conflict Detection Predictor
by Javier A. Pérez-Castán, Luis Pérez Sanz, Marta Fernández-Castellano, Tomislav Radišić, Kristina Samardžić and Ivan Tukarić
Sensors 2022, 22(19), 7680; https://doi.org/10.3390/s22197680 - 10 Oct 2022
Cited by 2 | Viewed by 1924
Abstract
Designing and developing artificial intelligence (AI)-based systems that can be trusted justifiably is one of the main issues aviation must face in the coming years. European Union Aviation Safety Agency (EASA) has developed a user guide that could be potentially transformed as means [...] Read more.
Designing and developing artificial intelligence (AI)-based systems that can be trusted justifiably is one of the main issues aviation must face in the coming years. European Union Aviation Safety Agency (EASA) has developed a user guide that could be potentially transformed as means of compliance for future AI-based regulation. Designers and developers must understand how the learning assurance process of any machine learning (ML) model impacts trust. ML is a narrow branch of AI that uses statistical models to perform predictions. This work deals with the learning assurance process for ML-based systems in the field of air traffic control. A conflict detection tool has been developed to identify separation infringements among aircraft pairs, and the ML algorithm used for classification and regression was extreme gradient boosting. This paper analyses the validity and adaptability of EASA W-shaped methodology for ML-based systems. The results have identified the lack of the EASA W-shaped methodology in time-dependent analysis, by showing how time can impact ML algorithms designed in the case where no time requirements are considered. Another meaningful conclusion is, for systems that depend highly on when the prediction is made, classification and regression metrics cannot be one-size-fits-all because they vary over time. Full article
(This article belongs to the Special Issue Sensors for Navigation and Control Systems)
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19 pages, 6918 KiB  
Article
Application of Initial Bias Estimation Method for Inertial Navigation System (INS)/Doppler Velocity Log (DVL) and INS/DVL/Gyrocompass Using Micro-Electro-Mechanical System Sensors
by Gen Fukuda and Nobuaki Kubo
Sensors 2022, 22(14), 5334; https://doi.org/10.3390/s22145334 - 17 Jul 2022
Cited by 5 | Viewed by 1914
Abstract
This article proposes a novel initial bias estimation method using a trajectory generator (TG). The accuracy of attitude and position estimation in navigation after using the inertial navigation system/Doppler velocity log (INS/DVL) and INS/DVL/gyrocompass (IDG) for 1 h were evaluated, and the results [...] Read more.
This article proposes a novel initial bias estimation method using a trajectory generator (TG). The accuracy of attitude and position estimation in navigation after using the inertial navigation system/Doppler velocity log (INS/DVL) and INS/DVL/gyrocompass (IDG) for 1 h were evaluated, and the results were compared to those obtained using the conventional Kalman filter (KF) estimation method. The probability of a horizontal position error < 1852 m (1 nautical mile) with a bias interval > 400 s was 100% and 9% for the TG and KF, respectively. In addition, the IDG average horizontal position errors over 1 h were 493 m and 507 m for the TG and KF, respectively. Moreover, the amount of variation was 2 m and 27 m for the TG and the KF, respectively. Thus, the proposed method is effective for initial bias estimation of INS/DVL and IDG using micro-electro-mechanical system sensors on a constantly moving vessel. Full article
(This article belongs to the Special Issue Sensors for Navigation and Control Systems)
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42 pages, 5409 KiB  
Article
An Extended Usability and UX Evaluation of a Mobile Application for the Navigation of Individuals with Blindness and Visual Impairments Outdoors—An Evaluation Framework Based on Training
by Paraskevi Theodorou, Kleomenis Tsiligkos, Apostolos Meliones and Costas Filios
Sensors 2022, 22(12), 4538; https://doi.org/10.3390/s22124538 - 16 Jun 2022
Cited by 12 | Viewed by 4541
Abstract
Navigation assistive technologies have been designed to support the mobility of people who are blind and visually impaired during independent navigation by providing sensory augmentation, spatial information and general awareness of their environment. This paper focuses on the extended Usability and User Experience [...] Read more.
Navigation assistive technologies have been designed to support the mobility of people who are blind and visually impaired during independent navigation by providing sensory augmentation, spatial information and general awareness of their environment. This paper focuses on the extended Usability and User Experience (UX) evaluation of BlindRouteVision, an outdoor navigation smartphone application that tries to efficiently solve problems related to the pedestrian navigation of visually impaired people without the aid of guides. The proposed system consists of an Android application that interacts with an external high-accuracy GPS sensor tracking pedestrian mobility in real-time, a second external device specifically designed to be mounted on traffic lights for identifying traffic light status and an ultrasonic sensor for detecting near-field obstacles along the route of the blind. Moreover, during outdoor navigation, it can optionally incorporate the use of Public Means of Transport, as well as provide multiple other uses such as dialing a call and notifying the current location in case of an emergency. We present findings from a Usability and UX standpoint of our proposed system conducted in the context of a pilot study, with 30 people having varying degrees of blindness. We also received feedback for improving both the available functionality of our application and the process by which the blind users learn the features of the application. The method of the study involved using standardized questionnaires and semi-structured interviews. The evaluation took place after the participants were exposed to the system’s functionality via specialized user-centered training sessions organized around a training version of the application that involves route simulation. The results indicate an overall positive attitude from the users. Full article
(This article belongs to the Special Issue Sensors for Navigation and Control Systems)
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20 pages, 4640 KiB  
Article
Study of Model Uncertainties Influence on the Impact Point Dispersion for a Gasodynamicaly Controlled Projectile
by Mariusz Jacewicz, Piotr Lichota, Dariusz Miedziński and Robert Głębocki
Sensors 2022, 22(9), 3257; https://doi.org/10.3390/s22093257 - 24 Apr 2022
Cited by 3 | Viewed by 1613
Abstract
The article presents the analysis of the impact point dispersion reduction using lateral correction thrusters. Two types of control algorithms are used and four sources of uncertainties are taken into account: aerodynamic parameters, thrust curve, initial conditions and IMU errors. The Monte Carlo [...] Read more.
The article presents the analysis of the impact point dispersion reduction using lateral correction thrusters. Two types of control algorithms are used and four sources of uncertainties are taken into account: aerodynamic parameters, thrust curve, initial conditions and IMU errors. The Monte Carlo approach was used for simulations and Circular Error Probable was used as a measure of dispersion. Generic rocket mathematical and simulation model was created in MATLAB/Simulink 2020b environment. Results show that the use of control algorithms greatly reduces the impact point dispersion. Full article
(This article belongs to the Special Issue Sensors for Navigation and Control Systems)
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Review

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22 pages, 1986 KiB  
Review
Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review
by Ndidiamaka Adiuku, Nicolas P. Avdelidis, Gilbert Tang and Angelos Plastropoulos
Sensors 2024, 24(5), 1377; https://doi.org/10.3390/s24051377 - 21 Feb 2024
Viewed by 824
Abstract
The field of learning-based navigation for mobile robots is experiencing a surge of interest from research and industry sectors. The application of this technology for visual aircraft inspection tasks within a maintenance, repair, and overhaul (MRO) hangar necessitates efficient perception and obstacle avoidance [...] Read more.
The field of learning-based navigation for mobile robots is experiencing a surge of interest from research and industry sectors. The application of this technology for visual aircraft inspection tasks within a maintenance, repair, and overhaul (MRO) hangar necessitates efficient perception and obstacle avoidance capabilities to ensure a reliable navigation experience. The present reliance on manual labour, static processes, and outdated technologies limits operation efficiency in the inherently dynamic and increasingly complex nature of the real-world hangar environment. The challenging environment limits the practical application of conventional methods and real-time adaptability to changes. In response to these challenges, recent years research efforts have witnessed advancement with machine learning integration aimed at enhancing navigational capability in both static and dynamic scenarios. However, most of these studies have not been specific to the MRO hangar environment, but related challenges have been addressed, and applicable solutions have been developed. This paper provides a comprehensive review of learning-based strategies with an emphasis on advancements in deep learning, object detection, and the integration of multiple approaches to create hybrid systems. The review delineates the application of learning-based methodologies to real-time navigational tasks, encompassing environment perception, obstacle detection, avoidance, and path planning through the use of vision-based sensors. The concluding section addresses the prevailing challenges and prospective development directions in this domain. Full article
(This article belongs to the Special Issue Sensors for Navigation and Control Systems)
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