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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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33 pages, 4823 KiB  
Article
NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio
by Panagiotis T. Karfakis, Micael S. Couceiro and David Portugal
Sensors 2023, 23(11), 5354; https://doi.org/10.3390/s23115354 - 5 Jun 2023
Cited by 5 | Viewed by 3143
Abstract
Robot localization is a crucial task in robotic systems and is a pre-requisite for navigation. In outdoor environments, Global Navigation Satellite Systems (GNSS) have aided towards this direction, alongside laser and visual sensing. Despite their application in the field, GNSS suffers from limited [...] Read more.
Robot localization is a crucial task in robotic systems and is a pre-requisite for navigation. In outdoor environments, Global Navigation Satellite Systems (GNSS) have aided towards this direction, alongside laser and visual sensing. Despite their application in the field, GNSS suffers from limited availability in dense urban and rural environments. Light Detection and Ranging (LiDAR), inertial and visual methods are also prone to drift and can be susceptible to outliers due to environmental changes and illumination conditions. In this work, we propose a cellular Simultaneous Localization and Mapping (SLAM) framework based on 5G New Radio (NR) signals and inertial measurements for mobile robot localization with several gNodeB stations. The method outputs the pose of the robot along with a radio signal map based on the Received Signal Strength Indicator (RSSI) measurements for correction purposes. We then perform benchmarking against LiDAR-Inertial Odometry Smoothing and Mapping (LIO-SAM), a state-of-the-art LiDAR SLAM method, comparing performance via a simulator ground truth reference. Two experimental setups are presented and discussed using the sub-6 GHz and mmWave frequency bands for communication, while the transmission is based on down-link (DL) signals. Our results show that 5G positioning can be utilized for radio SLAM, providing increased robustness in outdoor environments and demonstrating its potential to assist in robot localization, as an additional absolute source of information when LiDAR methods fail and GNSS data is unreliable. Full article
(This article belongs to the Special Issue Sensor Based Perception for Field Robotics)
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36 pages, 17376 KiB  
Article
Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar—A Feasibility Study
by Nastassia Vysotskaya, Christoph Will, Lorenzo Servadei, Noah Maul, Christian Mandl, Merlin Nau, Jens Harnisch and Andreas Maier
Sensors 2023, 23(8), 4111; https://doi.org/10.3390/s23084111 - 19 Apr 2023
Cited by 4 | Viewed by 4210
Abstract
Blood pressure monitoring is of paramount importance in the assessment of a human’s cardiovascular health. The state-of-the-art method remains the usage of an upper-arm cuff sphygmomanometer. However, this device suffers from severe limitations—it only provides a static blood pressure value pair, is incapable [...] Read more.
Blood pressure monitoring is of paramount importance in the assessment of a human’s cardiovascular health. The state-of-the-art method remains the usage of an upper-arm cuff sphygmomanometer. However, this device suffers from severe limitations—it only provides a static blood pressure value pair, is incapable of capturing blood pressure variations over time, is inaccurate, and causes discomfort upon use. This work presents a radar-based approach that utilizes the movement of the skin due to artery pulsation to extract pressure waves. From those waves, a set of 21 features was collected and used—together with the calibration parameters of age, gender, height, and weight—as input for a neural network-based regression model. After collecting data from 55 subjects from radar and a blood pressure reference device, we trained 126 networks to analyze the developed approach’s predictive power. As a result, a very shallow network with just two hidden layers produced a systolic error of 9.2±8.3 mmHg (mean error ± standard deviation) and a diastolic error of 7.7±5.7 mmHg. While the trained model did not reach the requirements of the AAMI and BHS blood pressure measuring standards, optimizing network performance was not the goal of the proposed work. Still, the approach has displayed great potential in capturing blood pressure variation with the proposed features. The presented approach therefore shows great potential to be incorporated into wearable devices for continuous blood pressure monitoring for home use or screening applications, after improving this approach even further. Full article
(This article belongs to the Special Issue Advanced Wearable Sensors Technologies for Healthcare Monitoring)
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28 pages, 1981 KiB  
Review
Biomimetic Approaches for Human Arm Motion Generation: Literature Review and Future Directions
by Urvish Trivedi, Dimitrios Menychtas, Redwan Alqasemi and Rajiv Dubey
Sensors 2023, 23(8), 3912; https://doi.org/10.3390/s23083912 - 12 Apr 2023
Cited by 3 | Viewed by 3543
Abstract
In recent years, numerous studies have been conducted to analyze how humans subconsciously optimize various performance criteria while performing a particular task, which has led to the development of robots that are capable of performing tasks with a similar level of efficiency as [...] Read more.
In recent years, numerous studies have been conducted to analyze how humans subconsciously optimize various performance criteria while performing a particular task, which has led to the development of robots that are capable of performing tasks with a similar level of efficiency as humans. The complexity of the human body has led researchers to create a framework for robot motion planning to recreate those motions in robotic systems using various redundancy resolution methods. This study conducts a thorough analysis of the relevant literature to provide a detailed exploration of the different redundancy resolution methodologies used in motion generation for mimicking human motion. The studies are investigated and categorized according to the study methodology and various redundancy resolution methods. An examination of the literature revealed a strong trend toward formulating intrinsic strategies that govern human movement through machine learning and artificial intelligence. Subsequently, the paper critically evaluates the existing approaches and highlights their limitations. It also identifies the potential research areas that hold promise for future investigations. Full article
(This article belongs to the Section Sensors and Robotics)
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25 pages, 4252 KiB  
Review
Towards an Evolved Immersive Experience: Exploring 5G- and Beyond-Enabled Ultra-Low-Latency Communications for Augmented and Virtual Reality
by Ananya Hazarika and Mehdi Rahmati
Sensors 2023, 23(7), 3682; https://doi.org/10.3390/s23073682 - 2 Apr 2023
Cited by 16 | Viewed by 7636
Abstract
Augmented reality and virtual reality technologies are witnessing an evolutionary change in the 5G and Beyond (5GB) network due to their promising ability to enable an immersive and interactive environment by coupling the virtual world with the real one. However, the requirement of [...] Read more.
Augmented reality and virtual reality technologies are witnessing an evolutionary change in the 5G and Beyond (5GB) network due to their promising ability to enable an immersive and interactive environment by coupling the virtual world with the real one. However, the requirement of low-latency connectivity, which is defined as the end-to-end delay between the action and the reaction, is very crucial to leverage these technologies for a high-quality immersive experience. This paper provides a comprehensive survey and detailed insight into various advantageous approaches from the hardware and software perspectives, as well as the integration of 5G technology, towards 5GB, in enabling a low-latency environment for AR and VR applications. The contribution of 5GB systems as an outcome of several cutting-edge technologies, such as massive multiple-input, multiple-output (mMIMO) and millimeter wave (mmWave), along with the utilization of artificial intelligence (AI) and machine learning (ML) techniques towards an ultra-low-latency communication system, is also discussed in this paper. The potential of using a visible-light communications (VLC)-guided beam through a learning algorithm for a futuristic, evolved immersive experience of augmented and virtual reality with the ultra-low-latency transmission of multi-sensory tracking information with an optimal scheduling policy is discussed in this paper. Full article
(This article belongs to the Special Issue Advanced Wireless Sensing Techniques for Communication)
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14 pages, 8451 KiB  
Article
An In-Ear PPG-Based Blood Glucose Monitor: A Proof-of-Concept Study
by Ghena Hammour and Danilo P. Mandic
Sensors 2023, 23(6), 3319; https://doi.org/10.3390/s23063319 - 21 Mar 2023
Cited by 9 | Viewed by 10548
Abstract
Monitoring diabetes saves lives. To this end, we introduce a novel, unobtrusive, and readily deployable in-ear device for the continuous and non-invasive measurement of blood glucose levels (BGLs). The device is equipped with a low-cost commercially available pulse oximeter whose infrared wavelength (880 [...] Read more.
Monitoring diabetes saves lives. To this end, we introduce a novel, unobtrusive, and readily deployable in-ear device for the continuous and non-invasive measurement of blood glucose levels (BGLs). The device is equipped with a low-cost commercially available pulse oximeter whose infrared wavelength (880 nm) is used for the acquisition of photoplethysmography (PPG). For rigor, we considered a full range of diabetic conditions (non-diabetic, pre-diabetic, type I diabetic, and type II diabetic). Recordings spanned nine different days, starting in the morning while fasting, up to a minimum of a two-hour period after eating a carbohydrate-rich breakfast. The BGLs from PPG were estimated using a suite of regression-based machine learning models, which were trained on characteristic features of PPG cycles pertaining to high and low BGLs. The analysis shows that, as desired, an average of 82% of the BGLs estimated from PPG lie in region A of the Clarke error grid (CEG) plot, with 100% of the estimated BGLs in the clinically acceptable CEG regions A and B. These results demonstrate the potential of the ear canal as a site for non-invasive blood glucose monitoring. Full article
(This article belongs to the Section Wearables)
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18 pages, 3601 KiB  
Article
Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction
by Bach-Tung Pham, Phuong Thi Le, Tzu-Chiang Tai, Yi-Chiung Hsu, Yung-Hui Li and Jia-Ching Wang
Sensors 2023, 23(6), 2993; https://doi.org/10.3390/s23062993 - 9 Mar 2023
Cited by 5 | Viewed by 4770
Abstract
An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is crucial for remote patient monitoring equipment. An accurate ECG signal classification is critical for real-time measurement, analysis, archiving, and transmission of clinical data. Numerous studies have focused on [...] Read more.
An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is crucial for remote patient monitoring equipment. An accurate ECG signal classification is critical for real-time measurement, analysis, archiving, and transmission of clinical data. Numerous studies have focused on accurate heartbeat classification, and deep neural networks have been suggested for better accuracy and simplicity. We investigated a new model for ECG heartbeat classification and found that it surpasses state-of-the-art models, achieving remarkable accuracy scores of 98.5% on the Physionet MIT-BIH dataset and 98.28% on the PTB database. Furthermore, our model achieves an impressive F1-score of approximately 86.71%, outperforming other models, such as MINA, CRNN, and EXpertRF on the PhysioNet Challenge 2017 dataset. Full article
(This article belongs to the Special Issue Sensors and Signal Processing for Biomedical Application)
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26 pages, 4081 KiB  
Article
AI-Enabled Smart Wristband Providing Real-Time Vital Signs and Stress Monitoring
by Nikos Mitro, Katerina Argyri, Lampros Pavlopoulos, Dimitrios Kosyvas, Lazaros Karagiannidis, Margarita Kostovasili, Fay Misichroni, Eleftherios Ouzounoglou and Angelos Amditis
Sensors 2023, 23(5), 2821; https://doi.org/10.3390/s23052821 - 4 Mar 2023
Cited by 8 | Viewed by 6552
Abstract
This work introduces the design, architecture, implementation, and testing of a low-cost and machine-learning-enabled device to be worn on the wrist. The suggested wearable device has been developed for use during emergency incidents of large passenger ship evacuations, and enables the real-time monitoring [...] Read more.
This work introduces the design, architecture, implementation, and testing of a low-cost and machine-learning-enabled device to be worn on the wrist. The suggested wearable device has been developed for use during emergency incidents of large passenger ship evacuations, and enables the real-time monitoring of the passengers’ physiological state, and stress detection. Based on a properly preprocessed PPG signal, the device provides essential biometric data (pulse rate and oxygen saturation level) and an efficient unimodal machine learning pipeline. The stress detecting machine learning pipeline is based on ultra-short-term pulse rate variability, and has been successfully integrated into the microcontroller of the developed embedded device. As a result, the presented smart wristband is able to provide real-time stress detection. The stress detection system has been trained with the use of the publicly available WESAD dataset, and its performance has been tested through a two-stage process. Initially, evaluation of the lightweight machine learning pipeline on a previously unseen subset of the WESAD dataset was performed, reaching an accuracy score equal to 91%. Subsequently, external validation was conducted, through a dedicated laboratory study of 15 volunteers subjected to well-acknowledged cognitive stressors while wearing the smart wristband, which yielded an accuracy score equal to 76%. Full article
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31 pages, 7945 KiB  
Review
State-of-the-Art Review on Wearable Obstacle Detection Systems Developed for Assistive Technologies and Footwear
by Anna M. Joseph, Azadeh Kian and Rezaul Begg
Sensors 2023, 23(5), 2802; https://doi.org/10.3390/s23052802 - 3 Mar 2023
Cited by 4 | Viewed by 5369
Abstract
Walking independently is essential to maintaining our quality of life but safe locomotion depends on perceiving hazards in the everyday environment. To address this problem, there is an increasing focus on developing assistive technologies that can alert the user to the risk destabilizing [...] Read more.
Walking independently is essential to maintaining our quality of life but safe locomotion depends on perceiving hazards in the everyday environment. To address this problem, there is an increasing focus on developing assistive technologies that can alert the user to the risk destabilizing foot contact with either the ground or obstacles, leading to a fall. Shoe-mounted sensor systems designed to monitor foot-obstacle interaction are being employed to identify tripping risk and provide corrective feedback. Advances in smart wearable technologies, integrating motion sensors with machine learning algorithms, has led to developments in shoe-mounted obstacle detection. The focus of this review is gait-assisting wearable sensors and hazard detection for pedestrians. This literature represents a research front that is critically important in paving the way towards practical, low-cost, wearable devices that can make walking safer and reduce the increasing financial and human costs of fall injuries. Full article
(This article belongs to the Special Issue Feature Papers in Wearables 2022)
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46 pages, 10529 KiB  
Review
Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects
by Halid Abdulrahim Kadi and Kasim Terzić
Sensors 2023, 23(5), 2389; https://doi.org/10.3390/s23052389 - 21 Feb 2023
Cited by 2 | Viewed by 4067
Abstract
Manipulating cloth-like deformable objects (CDOs) is a long-standing problem in the robotics community. CDOs are flexible (non-rigid) objects that do not show a detectable level of compression strength while two points on the article are pushed towards each other and include objects such [...] Read more.
Manipulating cloth-like deformable objects (CDOs) is a long-standing problem in the robotics community. CDOs are flexible (non-rigid) objects that do not show a detectable level of compression strength while two points on the article are pushed towards each other and include objects such as ropes (1D), fabrics (2D) and bags (3D). In general, CDOs’ many degrees of freedom (DoF) introduce severe self-occlusion and complex state–action dynamics as significant obstacles to perception and manipulation systems. These challenges exacerbate existing issues of modern robotic control methods such as imitation learning (IL) and reinforcement learning (RL). This review focuses on the application details of data-driven control methods on four major task families in this domain: cloth shaping, knot tying/untying, dressing and bag manipulation. Furthermore, we identify specific inductive biases in these four domains that present challenges for more general IL and RL algorithms. Full article
(This article belongs to the Section Sensors and Robotics)
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25 pages, 2878 KiB  
Review
LoRa Technology in Flying Ad Hoc Networks: A Survey of Challenges and Open Issues
by William David Paredes, Hemani Kaushal, Iman Vakilinia and Zornitza Prodanoff
Sensors 2023, 23(5), 2403; https://doi.org/10.3390/s23052403 - 21 Feb 2023
Cited by 9 | Viewed by 3092
Abstract
The Internet of Things (IoT) and Flying Ad Hoc Networks (FANETs) have become hot topics among researchers because of the increased availability of Unmanned Aerial Vehicles (UAVs) and the electronic components required to control and connect them (e.g., microcontrollers, single board computers, and [...] Read more.
The Internet of Things (IoT) and Flying Ad Hoc Networks (FANETs) have become hot topics among researchers because of the increased availability of Unmanned Aerial Vehicles (UAVs) and the electronic components required to control and connect them (e.g., microcontrollers, single board computers, and radios). LoRa is a wireless technology, intended for the IoT, that requires low power and provides long-range communications, which can be useful for ground and aerial applications. This paper explores the role that LoRa plays in FANET design by presenting a technical overview of both, and by performing a systematic literature review based on a breakdown of the communications, mobility and energy topics involved in a FANET implementation. Furthermore, open issues in protocol design are discussed, as well as other challenges associated with the use of LoRa in the deployment of FANETs. Full article
(This article belongs to the Section Sensor Networks)
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16 pages, 2680 KiB  
Article
Minimum-Time Trajectory Generation for Wheeled Mobile Systems Using Bézier Curves with Constraints on Velocity, Acceleration and Jerk
by Martina Benko Loknar, Gregor Klančar and Sašo Blažič
Sensors 2023, 23(4), 1982; https://doi.org/10.3390/s23041982 - 10 Feb 2023
Cited by 11 | Viewed by 2344
Abstract
This paper considers the problem of minimum-time smooth trajectory planning for wheeled mobile robots. The smooth path is defined by several Bézier curves and the calculated velocity profiles on individual segments are minimum-time with continuous velocity and acceleration in the joints. We describe [...] Read more.
This paper considers the problem of minimum-time smooth trajectory planning for wheeled mobile robots. The smooth path is defined by several Bézier curves and the calculated velocity profiles on individual segments are minimum-time with continuous velocity and acceleration in the joints. We describe a novel solution for the construction of a 5th order Bézier curve that enables a simple and intuitive parameterization. The proposed trajectory optimization considers environment space constraints and constraints on the velocity, acceleration, and jerk. The operation of the trajectory planning algorithm has been demonstrated in two simulations: on a racetrack and in a warehouse environment. Therefore, we have shown that the proposed path construction and trajectory generation algorithm can be applied to a constrained environment and can also be used in real-world driving scenarios. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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23 pages, 6358 KiB  
Article
Indoor 2D Positioning Method for Mobile Robots Based on the Fusion of RSSI and Magnetometer Fingerprints
by Peter Sarcevic, Dominik Csik and Akos Odry
Sensors 2023, 23(4), 1855; https://doi.org/10.3390/s23041855 - 7 Feb 2023
Cited by 16 | Viewed by 2639
Abstract
Received signal strength indicator (RSSI)-based fingerprinting is a widely used technique for indoor localization, but these methods suffer from high error rates due to various reflections, interferences, and noises. The use of disturbances in the magnetic field in indoor localization methods has gained [...] Read more.
Received signal strength indicator (RSSI)-based fingerprinting is a widely used technique for indoor localization, but these methods suffer from high error rates due to various reflections, interferences, and noises. The use of disturbances in the magnetic field in indoor localization methods has gained increasing attention in recent years, since this technology provides stable measurements with low random fluctuations. In this paper, a novel fingerprinting-based indoor 2D positioning method, which utilizes the fusion of RSSI and magnetometer measurements, is proposed for mobile robots. The method applies multilayer perceptron (MLP) feedforward neural networks to determine the 2D position, based on both the magnetometer data and the RSSI values measured between the mobile unit and anchor nodes. The magnetic field strength is measured on the mobile node, and it provides information about the disturbance levels in the given position. The proposed method is validated using data collected in two realistic indoor scenarios with multiple static objects. The magnetic field measurements are examined in three different combinations, i.e., the measurements of the three sensor axes are tested together, the magnetic field magnitude is used alone, and the Z-axis-based measurements are used together with the magnitude in the X-Y plane. The obtained results show that significant improvement can be achieved by fusing the two data types in scenarios where the magnetic field has high variance. The achieved results show that the improvement can be above 35% compared to results obtained by utilizing only RSSI or magnetic sensor data. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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33 pages, 3375 KiB  
Article
A Modular In-Vehicle C-ITS Architecture for Sensor Data Collection, Vehicular Communications and Cloud Connectivity
by David Rocha, Gil Teixeira, Emanuel Vieira, João Almeida and Joaquim Ferreira
Sensors 2023, 23(3), 1724; https://doi.org/10.3390/s23031724 - 3 Feb 2023
Cited by 13 | Viewed by 3120
Abstract
The growth of the automobile industry in recent decades and the overuse of personal vehicles have amplified problems directly related to road safety, such as the increase in traffic congestion and number of accidents, as well as the degradation of the quality of [...] Read more.
The growth of the automobile industry in recent decades and the overuse of personal vehicles have amplified problems directly related to road safety, such as the increase in traffic congestion and number of accidents, as well as the degradation of the quality of roads. At the same time, and with the contribution of climate change effects, dangerous weather events have become more common on road infrastructure. In this context, Cooperative Intelligent Transport Systems (C-ITS) and Internet of Things (IoT) solutions emerge to overcome the limitations of human and local sensory systems, through the collection and distribution of relevant data to Connected and Automated Vehicles (CAVs). In this paper, an intra- and inter-vehicle sensory data collection system is presented, starting with the acquisition of relevant data present on the Controller Area Network (CAN) bus, collected through the vehicle’s On-Board-Diagnostics II (OBD-II) port, as well as on an on-board smartphone device and possibly other additional sensors. Short-range communication technologies, such as Bluetooth Low Energy (BLE), Wi-Fi, and ITS-G5, are employed in conjunction with long-range cellular networks for data dissemination and remote cloud monitoring. The results of the experimental tests allow the analysis of the road environment, as well as the notification in near real-time of adverse road conditions to drivers. The developed data collection system reveals itself as a potentially valuable tool for improving road safety and to iterate on the current Road Weather Models (RWMs). Full article
(This article belongs to the Special Issue Sensor Networks for Vehicular Communications)
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11 pages, 3557 KiB  
Article
A Hydrogel-Based Electronic Skin for Touch Detection Using Electrical Impedance Tomography
by Huiyang Zhang, Anubha Kalra, Andrew Lowe, Yang Yu and Gautam Anand
Sensors 2023, 23(3), 1571; https://doi.org/10.3390/s23031571 - 1 Feb 2023
Cited by 7 | Viewed by 2442
Abstract
Recent advancement in wearable and robot-assisted healthcare technology gives rise to the demand for smart interfaces that allow more efficient human-machine interaction. In this paper, a hydrogel-based soft sensor for subtle touch detection is proposed. Adopting the working principle of a biomedical imaging [...] Read more.
Recent advancement in wearable and robot-assisted healthcare technology gives rise to the demand for smart interfaces that allow more efficient human-machine interaction. In this paper, a hydrogel-based soft sensor for subtle touch detection is proposed. Adopting the working principle of a biomedical imaging technology known as electrical impedance tomography (EIT), the sensor produces images that display the electrical conductivity distribution of its sensitive region to enable touch detection. The sensor was made from a natural gelatin hydrogel whose electrical conductivity is considerably less than that of human skin. The low conductivity of the sensor enabled a touch-detection mechanism based on a novel short-circuiting approach, which resulted in the reconstructed images being predominantly affected by the electrical contact between the sensor and fingertips, rather than the conventionally used piezoresistive response of the sensing material. The experimental results indicated that the proposed sensor was promising for detecting subtle contacts without the necessity of exerting a noticeable force on the sensor. Full article
(This article belongs to the Special Issue Interactive, Mobile, Wearable Sensors and Technology for Elderly Care)
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22 pages, 6249 KiB  
Article
Prehensile and Non-Prehensile Robotic Pick-and-Place of Objects in Clutter Using Deep Reinforcement Learning
by Muhammad Babar Imtiaz, Yuansong Qiao and Brian Lee
Sensors 2023, 23(3), 1513; https://doi.org/10.3390/s23031513 - 29 Jan 2023
Cited by 7 | Viewed by 4646
Abstract
In this study, we develop a framework for an intelligent and self-supervised industrial pick-and-place operation for cluttered environments. Our target is to have the agent learn to perform prehensile and non-prehensile robotic manipulations to improve the efficiency and throughput of the pick-and-place task. [...] Read more.
In this study, we develop a framework for an intelligent and self-supervised industrial pick-and-place operation for cluttered environments. Our target is to have the agent learn to perform prehensile and non-prehensile robotic manipulations to improve the efficiency and throughput of the pick-and-place task. To achieve this target, we specify the problem as a Markov decision process (MDP) and deploy a deep reinforcement learning (RL) temporal difference model-free algorithm known as the deep Q-network (DQN). We consider three actions in our MDP; one is ‘grasping’ from the prehensile manipulation category and the other two are ‘left-slide’ and ‘right-slide’ from the non-prehensile manipulation category. Our DQN is composed of three fully convolutional networks (FCN) based on the memory-efficient architecture of DenseNet-121 which are trained together without causing any bottleneck situations. Each FCN corresponds to each discrete action and outputs a pixel-wise map of affordances for the relevant action. Rewards are allocated after every forward pass and backpropagation is carried out for weight tuning in the corresponding FCN. In this manner, non-prehensile manipulations are learnt which can, in turn, lead to possible successful prehensile manipulations in the near future and vice versa, thus increasing the efficiency and throughput of the pick-and-place task. The Results section shows performance comparisons of our approach to a baseline deep learning approach and a ResNet architecture-based approach, along with very promising test results at varying clutter densities across a range of complex scenario test cases. Full article
(This article belongs to the Special Issue Sensors for Robots II)
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18 pages, 5176 KiB  
Article
Energy Balance of Wireless Sensor Nodes Based on Bluetooth Low Energy and Thermoelectric Energy Harvesting
by Yuming Liu, Jordi-Roger Riba and Manuel Moreno-Eguilaz
Sensors 2023, 23(3), 1480; https://doi.org/10.3390/s23031480 - 28 Jan 2023
Cited by 10 | Viewed by 1869
Abstract
The internet of things (IoT) makes it possible to measure physical variables and acquire data in places that were impossible a few years ago, such as transmission lines and electrical substations. Monitoring and fault diagnosis strategies can then be applied. A battery or [...] Read more.
The internet of things (IoT) makes it possible to measure physical variables and acquire data in places that were impossible a few years ago, such as transmission lines and electrical substations. Monitoring and fault diagnosis strategies can then be applied. A battery or an energy harvesting system charging a rechargeable battery typically powers IoT devices. The energy harvesting unit and rechargeable battery supply the sensors and wireless communications modules. Therefore, the energy harvesting unit must be correctly sized to optimize the availability and reliability of IoT devices. This paper applies a power balance of the entire IoT device, including the energy harvesting module that includes two thermoelectric generators and a DC–DC converter, the battery, and the sensors and communication modules. Due to the small currents typical of the different communication phases and their fast-switching nature, it is not trivial to measure the energy in each phase, requiring very specific instrumentation. This work shows that using conventional instrumentation it is possible to measure the energy involved in the different modes of communication. A detailed energy balance of the battery is also carried out during charge and discharge cycles, as well as communication modes, from which the maximum allowable data transfer rate is determined. The approach presented here can be generalized to many other smart grid IoT devices. Full article
(This article belongs to the Special Issue Challenges in Energy Perspective on Mobile Sensor Networks)
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17 pages, 2068 KiB  
Article
On the Design of a Network Digital Twin for the Radio Access Network in 5G and Beyond
by Irene Vilà, Oriol Sallent and Jordi Pérez-Romero
Sensors 2023, 23(3), 1197; https://doi.org/10.3390/s23031197 - 20 Jan 2023
Cited by 6 | Viewed by 3131
Abstract
A Network Digital Twin (NDT) is a high-fidelity digital mirror of a real network. Given the increasing complexity of 5G and beyond networks, the use of an NDT becomes useful as a platform for testing configurations and algorithms prior to their application in [...] Read more.
A Network Digital Twin (NDT) is a high-fidelity digital mirror of a real network. Given the increasing complexity of 5G and beyond networks, the use of an NDT becomes useful as a platform for testing configurations and algorithms prior to their application in the real network, as well as for predicting the performance of such algorithms under different conditions. While an NDT can be defined for the different subsystems of the network, this paper proposes an NDT architecture focusing on the Radio Access Network (RAN), describing the components to represent and model the operation of the different RAN elements, and to perform emulations. Different application use cases are identified, and among them, the paper puts the focus on the training of Reinforcement Learning (RL) solutions for the RAN. For this use case, the paper introduces a framework aligned with O-RAN specifications and discusses the functionalities needed to integrate the NDT. This use case is illustrated with the description of a RAN NDT implementation used for training an RL-based capacity-sharing solution for network slicing. Presented results demonstrate that the implemented RAN NDT is a suitable platform to successfully train the RL solution, achieving service-level agreement satisfaction values above 85%. Full article
(This article belongs to the Special Issue State of the Art in beyond 5G and 6G Radio Communications Networks)
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15 pages, 1471 KiB  
Review
Robotic Technology in Foot and Ankle Surgery: A Comprehensive Review
by Taylor P. Stauffer, Billy I. Kim, Caitlin Grant, Samuel B. Adams and Albert T. Anastasio
Sensors 2023, 23(2), 686; https://doi.org/10.3390/s23020686 - 6 Jan 2023
Cited by 4 | Viewed by 3361
Abstract
Recent developments in robotic technologies in the field of orthopaedic surgery have largely been focused on higher volume arthroplasty procedures, with a paucity of attention paid to robotic potential for foot and ankle surgery. The aim of this paper is to summarize past [...] Read more.
Recent developments in robotic technologies in the field of orthopaedic surgery have largely been focused on higher volume arthroplasty procedures, with a paucity of attention paid to robotic potential for foot and ankle surgery. The aim of this paper is to summarize past and present developments foot and ankle robotics and describe outcomes associated with these interventions, with specific emphasis on the following topics: translational and preclinical utilization of robotics, deep learning and artificial intelligence modeling in foot and ankle, current applications for robotics in foot and ankle surgery, and therapeutic and orthotic-related utilizations of robotics related to the foot and ankle. Herein, we describe numerous recent robotic advancements across foot and ankle surgery, geared towards optimizing intra-operative performance, improving detection of foot and ankle pathology, understanding ankle kinematics, and rehabilitating post-surgically. Future research should work to incorporate robotics specifically into surgical procedures as other specialties within orthopaedics have done, and to further individualize machinery to patients, with the ultimate goal to improve perioperative and post-operative outcomes. Full article
(This article belongs to the Special Issue Medical Robotics 2022-2023)
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20 pages, 9638 KiB  
Article
Grass Cutting Robot for Inclined Surfaces in Hilly and Mountainous Areas
by Yuki Nishimura and Tomoyuki Yamaguchi
Sensors 2023, 23(1), 528; https://doi.org/10.3390/s23010528 - 3 Jan 2023
Cited by 2 | Viewed by 4121
Abstract
Grass cutting is necessary to prevent grass from diverting essential nutrients and water from crops. Usually, in hilly and mountainous areas, grass cutting is performed on steep slopes with an inclination angle of up to 60° (inclination gradient of 173%). However, such grass [...] Read more.
Grass cutting is necessary to prevent grass from diverting essential nutrients and water from crops. Usually, in hilly and mountainous areas, grass cutting is performed on steep slopes with an inclination angle of up to 60° (inclination gradient of 173%). However, such grass cutting tasks are dangerous owing to the unstable positioning of workers. For robots to perform these grass cutting tasks, slipping and falling must be prevented on inclined surfaces. In this study, a robot based on stable propeller control and four-wheel steering was developed to provide stable locomotion during grass cutting tasks. The robot was evaluated in terms of locomotion for different steering methods, straight motion on steep slopes, climbing ability, and coverage area. The results revealed that the robot was capable of navigating uneven terrains with steep slope angles. Moreover, no slipping actions that could have affected the grass cutting operations were observed. We confirmed that the proposed robot is able to cover 99.95% and 98.45% of an area on a rubber and grass slope, respectively. Finally, the robot was tested on different slopes with different angles in hilly and mountainous areas. The developed robot was able to perform the grass cutting task as expected. Full article
(This article belongs to the Special Issue Sensors and Robotic Systems for Agriculture Applications)
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18 pages, 3816 KiB  
Article
Deep Learning Framework for Controlling Work Sequence in Collaborative Human–Robot Assembly Processes
by Pedro P. Garcia, Telmo G. Santos, Miguel A. Machado and Nuno Mendes
Sensors 2023, 23(1), 553; https://doi.org/10.3390/s23010553 - 3 Jan 2023
Cited by 7 | Viewed by 2946
Abstract
The human–robot collaboration (HRC) solutions presented so far have the disadvantage that the interaction between humans and robots is based on the human’s state or on specific gestures purposely performed by the human, thus increasing the time required to perform a task and [...] Read more.
The human–robot collaboration (HRC) solutions presented so far have the disadvantage that the interaction between humans and robots is based on the human’s state or on specific gestures purposely performed by the human, thus increasing the time required to perform a task and slowing down the pace of human labor, making such solutions uninteresting. In this study, a different concept of the HRC system is introduced, consisting of an HRC framework for managing assembly processes that are executed simultaneously or individually by humans and robots. This HRC framework based on deep learning models uses only one type of data, RGB camera data, to make predictions about the collaborative workspace and human action, and consequently manage the assembly process. To validate the HRC framework, an industrial HRC demonstrator was built to assemble a mechanical component. Four different HRC frameworks were created based on the convolutional neural network (CNN) model structures: Faster R-CNN ResNet-50 and ResNet-101, YOLOv2 and YOLOv3. The HRC framework with YOLOv3 structure showed the best performance, showing a mean average performance of 72.26% and allowed the HRC industrial demonstrator to successfully complete all assembly tasks within a desired time window. The HRC framework has proven effective for industrial assembly applications. Full article
(This article belongs to the Special Issue Sensors for Robotic Applications in Europe)
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30 pages, 7537 KiB  
Article
Adsorbed Oxygen Ions and Oxygen Vacancies: Their Concentration and Distribution in Metal Oxide Chemical Sensors and Influencing Role in Sensitivity and Sensing Mechanisms
by Engin Ciftyurek, Zheshen Li and Klaus Schierbaum
Sensors 2023, 23(1), 29; https://doi.org/10.3390/s23010029 - 20 Dec 2022
Cited by 22 | Viewed by 4361
Abstract
Oxidation reactions on semiconducting metal oxide (SMOs) surfaces have been extensively worked on in catalysis, fuel cells, and sensors. SMOs engage powerfully in energy-related applications such as batteries, supercapacitors, solid oxide fuel cells (SOFCs), and sensors. A deep understanding of SMO surface and [...] Read more.
Oxidation reactions on semiconducting metal oxide (SMOs) surfaces have been extensively worked on in catalysis, fuel cells, and sensors. SMOs engage powerfully in energy-related applications such as batteries, supercapacitors, solid oxide fuel cells (SOFCs), and sensors. A deep understanding of SMO surface and oxygen interactions and defect engineering has become significant because all of the above-mentioned applications are based on the adsorption/absorption and consumption/transportation of adsorbed (physisorbed-chemisorbed) oxygen. More understanding of adsorbed oxygen and oxygen vacancies (VO,VO) is needed, as the former is the vital requirement for sensing chemical reactions, while the latter facilitates the replenishment of adsorbed oxygen ions on the surface. We determined the relation between sensor response (sensitivity) and the amounts of adsorbed oxygen ions (O2(ads), O(ads), O2(ads)2, O(ads)2), water/hydroxide groups (H2O/OH), oxygen vacancies (VO, VO), and ordinary lattice oxygen ions (Olattice2) as a function of temperature. During hydrogen (H2) testing, the different oxidation states (W6+, W5+, and W4+) of WO3 were quantified and correlated with oxygen vacancy formation (VO, VO). We used a combined application of XPS, UPS, XPEEM-LEEM, and chemical, electrical, and sensory analysis for H2 sensing. The sensor response was extraordinarily high: 424 against H2 at a temperature of 250 °C was recorded and explained on the basis of defect engineering, including oxygen vacancies and chemisorbed oxygen ions and surface stoichiometry of WO3. We established a correlation between the H2 sensing mechanism of WO3, sensor signal magnitude, the amount of adsorbed oxygen ions, and sensor testing temperature. This paper also provides a review of the detection, quantification, and identification of different adsorbed oxygen species. The different surface and bulk-sensitive characterization techniques relevant to analyzing the SMOs-based sensor are tabulated, providing the sensor designer with the chemical, physical, and electronic information extracted from each technique. Full article
(This article belongs to the Collection Gas Sensors)
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15 pages, 7710 KiB  
Article
A GNSS/INS/LiDAR Integration Scheme for UAV-Based Navigation in GNSS-Challenging Environments
by Ahmed Elamin, Nader Abdelaziz and Ahmed El-Rabbany
Sensors 2022, 22(24), 9908; https://doi.org/10.3390/s22249908 - 16 Dec 2022
Cited by 17 | Viewed by 3612
Abstract
Unmanned aerial vehicle (UAV) navigation has recently been the focus of many studies. The most challenging aspect of UAV navigation is maintaining accurate and reliable pose estimation. In outdoor environments, global navigation satellite systems (GNSS) are typically used for UAV localization. However, relying [...] Read more.
Unmanned aerial vehicle (UAV) navigation has recently been the focus of many studies. The most challenging aspect of UAV navigation is maintaining accurate and reliable pose estimation. In outdoor environments, global navigation satellite systems (GNSS) are typically used for UAV localization. However, relying solely on GNSS might pose safety risks in the event of receiver malfunction or antenna installation error. In this research, an unmanned aerial system (UAS) employing the Applanix APX15 GNSS/IMU board, a Velodyne Puck LiDAR sensor, and a Sony a7R II high-resolution camera was used to collect data for the purpose of developing a multi-sensor integration system. Unfortunately, due to a malfunctioning GNSS antenna, there were numerous prolonged GNSS signal outages. As a result, the GNSS/INS processing failed after obtaining an error that exceeded 25 km. To resolve this issue and to recover the precise trajectory of the UAV, a GNSS/INS/LiDAR integrated navigation system was developed. The LiDAR data were first processed using the optimized LOAM SLAM algorithm, which yielded the position and orientation estimates. Pix4D Mapper software was then used to process the camera images in the presence of ground control points (GCPs), which resulted in the precise camera positions and orientations that served as ground truth. All sensor data were timestamped by GPS, and all datasets were sampled at 10 Hz to match those of the LiDAR scans. Two case studies were considered, namely complete GNSS outage and assistance from GNSS PPP solution. In comparison to the complete GNSS outage, the results for the second case study were significantly improved. The improvement is described in terms of RMSE reductions of approximately 51% and 78% for the horizontal and vertical directions, respectively. Additionally, the RMSE of the roll and yaw angles was reduced by 13% and 30%, respectively. However, the RMSE of the pitch angle was increased by about 13%. Full article
(This article belongs to the Section Navigation and Positioning)
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25 pages, 3483 KiB  
Article
A Wildfire Smoke Detection System Using Unmanned Aerial Vehicle Images Based on the Optimized YOLOv5
by Mukhriddin Mukhiddinov, Akmalbek Bobomirzaevich Abdusalomov and Jinsoo Cho
Sensors 2022, 22(23), 9384; https://doi.org/10.3390/s22239384 - 1 Dec 2022
Cited by 33 | Viewed by 6413
Abstract
Wildfire is one of the most significant dangers and the most serious natural catastrophe, endangering forest resources, animal life, and the human economy. Recent years have witnessed a rise in wildfire incidents. The two main factors are persistent human interference with the natural [...] Read more.
Wildfire is one of the most significant dangers and the most serious natural catastrophe, endangering forest resources, animal life, and the human economy. Recent years have witnessed a rise in wildfire incidents. The two main factors are persistent human interference with the natural environment and global warming. Early detection of fire ignition from initial smoke can help firefighters react to such blazes before they become difficult to handle. Previous deep-learning approaches for wildfire smoke detection have been hampered by small or untrustworthy datasets, making it challenging to extrapolate the performances to real-world scenarios. In this study, we propose an early wildfire smoke detection system using unmanned aerial vehicle (UAV) images based on an improved YOLOv5. First, we curated a 6000-wildfire image dataset using existing UAV images. Second, we optimized the anchor box clustering using the K-mean++ technique to reduce classification errors. Then, we improved the network’s backbone using a spatial pyramid pooling fast-plus layer to concentrate small-sized wildfire smoke regions. Third, a bidirectional feature pyramid network was applied to obtain a more accessible and faster multi-scale feature fusion. Finally, network pruning and transfer learning approaches were implemented to refine the network architecture and detection speed, and correctly identify small-scale wildfire smoke areas. The experimental results proved that the proposed method achieved an average precision of 73.6% and outperformed other one- and two-stage object detectors on a custom image dataset. Full article
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29 pages, 9493 KiB  
Review
Visual SLAM: What Are the Current Trends and What to Expect?
by Ali Tourani, Hriday Bavle, Jose Luis Sanchez-Lopez and Holger Voos
Sensors 2022, 22(23), 9297; https://doi.org/10.3390/s22239297 - 29 Nov 2022
Cited by 32 | Viewed by 15316
Abstract
In recent years, Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gain. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map reconstruction and are [...] Read more.
In recent years, Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gain. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map reconstruction and are preferred over Light Detection And Ranging (LiDAR)-based methods due to their lighter weight, lower acquisition costs, and richer environment representation. Hence, several VSLAM approaches have evolved using different camera types (e.g., monocular or stereo), and have been tested on various datasets (e.g., Technische Universität München (TUM) RGB-D or European Robotics Challenge (EuRoC)) and in different conditions (i.e., indoors and outdoors), and employ multiple methodologies to have a better understanding of their surroundings. The mentioned variations have made this topic popular for researchers and have resulted in various methods. In this regard, the primary intent of this paper is to assimilate the wide range of works in VSLAM and present their recent advances, along with discussing the existing challenges and trends. This survey is worthwhile to give a big picture of the current focuses in robotics and VSLAM fields based on the concentrated resolutions and objectives of the state-of-the-art. This paper provides an in-depth literature survey of fifty impactful articles published in the VSLAMs domain. The mentioned manuscripts have been classified by different characteristics, including the novelty domain, objectives, employed algorithms, and semantic level. The paper also discusses the current trends and contemporary directions of VSLAM techniques that may help researchers investigate them. Full article
(This article belongs to the Special Issue Aerial Robotics: Navigation and Path Planning)
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20 pages, 7843 KiB  
Article
A Force-Feedback Methodology for Teleoperated Suturing Task in Robotic-Assisted Minimally Invasive Surgery
by Armin Ehrampoosh, Bijan Shirinzadeh, Joshua Pinskier, Julian Smith, Randall Moshinsky and Yongmin Zhong
Sensors 2022, 22(20), 7829; https://doi.org/10.3390/s22207829 - 14 Oct 2022
Cited by 11 | Viewed by 2876
Abstract
With robotic-assisted minimally invasive surgery (RAMIS), patients and surgeons benefit from a reduced incision size and dexterous instruments. However, current robotic surgery platforms lack haptic feedback, which is an essential element of safe operation. Moreover, teleportation control challenges make complex surgical tasks like [...] Read more.
With robotic-assisted minimally invasive surgery (RAMIS), patients and surgeons benefit from a reduced incision size and dexterous instruments. However, current robotic surgery platforms lack haptic feedback, which is an essential element of safe operation. Moreover, teleportation control challenges make complex surgical tasks like suturing more time-consuming than those that use manual tools. This paper presents a new force-sensing instrument that semi-automates the suturing task and facilitates teleoperated robotic manipulation. In order to generate the ideal needle insertion trajectory and pass the needle through its curvature, the end-effector mechanism has a rotating degree of freedom. Impedance control was used to provide sensory information about needle–tissue interaction forces to the operator using an indirect force estimation approach based on data-based models. The operator’s motion commands were then regulated using a hyperplanar virtual fixture (VF) designed to maintain the desired distance between the end-effector and tissue surface while avoiding unwanted contact. To construct the geometry of the VF, an optoelectronic sensor-based approach was developed. Based on the experimental investigation of the hyperplane VF methodology, improved needle–tissue interaction force, manipulation accuracy, and task completion times were demonstrated. Finally, experimental validation of the trained force estimation models and the perceived interaction forces by the user was conducted using online data, demonstrating the potential of the developed approach in improving task performance. Full article
(This article belongs to the Special Issue Robotics and Haptics: Haptic Feedback for Medical Robots)
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38 pages, 1958 KiB  
Article
E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures
by Athanasia Zlatintsi, Panagiotis P. Filntisis, Christos Garoufis, Niki Efthymiou, Petros Maragos, Andreas Menychtas, Ilias Maglogiannis, Panayiotis Tsanakas, Thomas Sounapoglou, Emmanouil Kalisperakis, Thomas Karantinos, Marina Lazaridi, Vasiliki Garyfalli, Asimakis Mantas, Leonidas Mantonakis and Nikolaos Smyrnis
Sensors 2022, 22(19), 7544; https://doi.org/10.3390/s22197544 - 5 Oct 2022
Cited by 11 | Viewed by 3078
Abstract
Wearable technologies and digital phenotyping foster unique opportunities for designing novel intelligent electronic services that can address various well-being issues in patients with mental disorders (i.e., schizophrenia and bipolar disorder), thus having the potential to revolutionize psychiatry and its clinical practice. In this [...] Read more.
Wearable technologies and digital phenotyping foster unique opportunities for designing novel intelligent electronic services that can address various well-being issues in patients with mental disorders (i.e., schizophrenia and bipolar disorder), thus having the potential to revolutionize psychiatry and its clinical practice. In this paper, we present e-Prevention, an innovative integrated system for medical support that facilitates effective monitoring and relapse prevention in patients with mental disorders. The technologies offered through e-Prevention include: (i) long-term continuous recording of biometric and behavioral indices through a smartwatch; (ii) video recordings of patients while being interviewed by a clinician, using a tablet; (iii) automatic and systematic storage of these data in a dedicated Cloud server and; (iv) the ability of relapse detection and prediction. This paper focuses on the description of the e-Prevention system and the methodologies developed for the identification of feature representations that correlate with and can predict psychopathology and relapses in patients with mental disorders. Specifically, we tackle the problem of relapse detection and prediction using Machine and Deep Learning techniques on all collected data. The results are promising, indicating that such predictions could be made and leading eventually to the prediction of psychopathology and the prevention of relapses. Full article
(This article belongs to the Special Issue AI for Biomedical Sensing and Imaging)
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25 pages, 4775 KiB  
Review
Optical Fiber Sensors and Sensing Networks: Overview of the Main Principles and Applications
by Cristiano Pendão and Ivo Silva
Sensors 2022, 22(19), 7554; https://doi.org/10.3390/s22197554 - 5 Oct 2022
Cited by 55 | Viewed by 9368
Abstract
Optical fiber sensors present several advantages in relation to other types of sensors. These advantages are essentially related to the optical fiber properties, i.e., small, lightweight, resistant to high temperatures and pressure, electromagnetically passive, among others. Sensing is achieved by exploring the properties [...] Read more.
Optical fiber sensors present several advantages in relation to other types of sensors. These advantages are essentially related to the optical fiber properties, i.e., small, lightweight, resistant to high temperatures and pressure, electromagnetically passive, among others. Sensing is achieved by exploring the properties of light to obtain measurements of parameters, such as temperature, strain, or angular velocity. In addition, optical fiber sensors can be used to form an Optical Fiber Sensing Network (OFSN) allowing manufacturers to create versatile monitoring solutions with several applications, e.g., periodic monitoring along extensive distances (kilometers), in extreme or hazardous environments, inside structures and engines, in clothes, and for health monitoring and assistance. Most of the literature available on this subject focuses on a specific field of optical sensing applications and details their principles of operation. This paper presents a more broad overview, providing the reader with a literature review that describes the main principles of optical sensing and highlights the versatility, advantages, and different real-world applications of optical sensing. Moreover, it includes an overview and discussion of a less common architecture, where optical sensing and Wireless Sensor Networks (WSNs) are integrated to harness the benefits of both worlds. Full article
(This article belongs to the Special Issue Optical Fiber Technology and Sensors)
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16 pages, 1176 KiB  
Review
From Identification to Sensing: RFID Is One of the Key Technologies in the IoT Field
by Yvan Duroc
Sensors 2022, 22(19), 7523; https://doi.org/10.3390/s22197523 - 4 Oct 2022
Cited by 11 | Viewed by 4366
Abstract
RFID (radio frequency identification) technology appeared nearly 70 years ago. Deployed more widely only from the early 2000s, it is now booming and its development is still accelerating. As its name indicates, its original function was the identification (of objects, animals, people) and [...] Read more.
RFID (radio frequency identification) technology appeared nearly 70 years ago. Deployed more widely only from the early 2000s, it is now booming and its development is still accelerating. As its name indicates, its original function was the identification (of objects, animals, people) and its applications were then essentially aimed at traceability, access control and logistics. If this type of use is still relevant today with more and more new application contexts and more and more efficient RFID tags, RFID has also evolved by integrating new capabilities. These new tags, known as augmented tags, include an information capture function. With the explosion of connected objects and the emergence of the Internet of Things (IoT), this old technology that is RFID still has a promising future and will probably be more and more present in our private and professional environments in all fields: logistics, industry, agriculture, building, health and even space. Full article
(This article belongs to the Special Issue RFID-Based Sensors)
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24 pages, 11672 KiB  
Article
The Use of Soil Moisture and Pore-Water Pressure Sensors for the Interpretation of Landslide Behavior in Small-Scale Physical Models
by Josip Peranić, Nina Čeh and Željko Arbanas
Sensors 2022, 22(19), 7337; https://doi.org/10.3390/s22197337 - 27 Sep 2022
Cited by 13 | Viewed by 3499
Abstract
This paper presents some of the results and experiences in monitoring the hydraulic response of downscaled slope models under simulated rainfall in 1 g. The downscaled slope model platform was developed as part of a four-year research project, “Physical modeling of landslide remediation [...] Read more.
This paper presents some of the results and experiences in monitoring the hydraulic response of downscaled slope models under simulated rainfall in 1 g. The downscaled slope model platform was developed as part of a four-year research project, “Physical modeling of landslide remediation constructions’ behavior under static and seismic actions”, and its main components are briefly described with the particular focus on the sensor network that allows monitoring changes in soil moisture and pore-water pressure (pwp). The technical characteristics of the sensors and the measurement methods used to provide the metrics are described in detail. Some data on the hydraulic and mechanical responses obtained from the conducted tests on slope models built from different soil types under different test conditions are presented and interpreted in the context of rainfall-induced landslides. The results show that the sensor network used is suitable for monitoring changes in the soil moisture and pwp in the model, both in terms of the transient rainfall infiltration through partially saturated soil and in terms of the rise in the water table and pwp build-up under fully saturated conditions. It is shown how simultaneous monitoring of soil moisture and pwp can be used to reconstruct stress paths that the monitored points undergo during different test phases. Finally, some peculiarities related to hydraulic hysteresis and surface erosion that were observed in some of tests are discussed, as well as possible difficulties in achieving and maintaining the targeted initial moisture distribution in slope models. Full article
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13 pages, 3261 KiB  
Article
The Development of a Digital Twin Framework for an Industrial Robotic Drilling Process
by Ahmad Farhadi, Stephen K. H. Lee, Eoin P. Hinchy, Noel P. O’Dowd and Conor T. McCarthy
Sensors 2022, 22(19), 7232; https://doi.org/10.3390/s22197232 - 23 Sep 2022
Cited by 16 | Viewed by 3634
Abstract
A digital twin is a digital representation of a physical entity that is updated in real-time by transfer of data between physical and digital (virtual) entities. In this manuscript we aim to introduce a digital twin framework for robotic drilling. Initially, a generic [...] Read more.
A digital twin is a digital representation of a physical entity that is updated in real-time by transfer of data between physical and digital (virtual) entities. In this manuscript we aim to introduce a digital twin framework for robotic drilling. Initially, a generic reference model is proposed to highlight elements of the digital twin relevant to robotic drilling. Then, a precise reference digital twin architecture model is developed, based on available standards and technologies. Finally, real-time visualisation of drilling process parameters is demonstrated as an initial step towards implementing a digital twin of a robotic drilling process. Full article
(This article belongs to the Special Issue Digital Twins, Sensing Technologies and Automation in Industry 4.0)
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49 pages, 19636 KiB  
Review
Sensing with Femtosecond Laser Filamentation
by Pengfei Qi, Wenqi Qian, Lanjun Guo, Jiayun Xue, Nan Zhang, Yuezheng Wang, Zhi Zhang, Zeliang Zhang, Lie Lin, Changlin Sun, Liguo Zhu and Weiwei Liu
Sensors 2022, 22(18), 7076; https://doi.org/10.3390/s22187076 - 19 Sep 2022
Cited by 19 | Viewed by 6184
Abstract
Femtosecond laser filamentation is a unique nonlinear optical phenomenon when high-power ultrafast laser propagation in all transparent optical media. During filamentation in the atmosphere, the ultrastrong field of 1013–1014 W/cm2 with a large distance ranging from meter to kilometers [...] Read more.
Femtosecond laser filamentation is a unique nonlinear optical phenomenon when high-power ultrafast laser propagation in all transparent optical media. During filamentation in the atmosphere, the ultrastrong field of 1013–1014 W/cm2 with a large distance ranging from meter to kilometers can effectively ionize, break, and excite the molecules and fragments, resulting in characteristic fingerprint emissions, which provide a great opportunity for investigating strong-field molecules interaction in complicated environments, especially remote sensing. Additionally, the ultrastrong intensity inside the filament can damage almost all the detectors and ignite various intricate higher order nonlinear optical effects. These extreme physical conditions and complicated phenomena make the sensing and controlling of filamentation challenging. This paper mainly focuses on recent research advances in sensing with femtosecond laser filamentation, including fundamental physics, sensing and manipulating methods, typical filament-based sensing techniques and application scenarios, opportunities, and challenges toward the filament-based remote sensing under different complicated conditions. Full article
(This article belongs to the Special Issue Sensing with Femtosecond Laser Filamentation)
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26 pages, 7233 KiB  
Article
Cloud Data-Driven Intelligent Monitoring System for Interactive Smart Farming
by Kristina Dineva and Tatiana Atanasova
Sensors 2022, 22(17), 6566; https://doi.org/10.3390/s22176566 - 31 Aug 2022
Cited by 19 | Viewed by 3873
Abstract
Smart farms, as a part of high-tech agriculture, collect a huge amount of data from IoT devices about the conditions of animals, plants, and the environment. These data are most often stored locally and are not used in intelligent monitoring systems to provide [...] Read more.
Smart farms, as a part of high-tech agriculture, collect a huge amount of data from IoT devices about the conditions of animals, plants, and the environment. These data are most often stored locally and are not used in intelligent monitoring systems to provide opportunities for extracting meaningful knowledge for the farmers. This often leads to a sense of missed transparency, fairness, and accountability, and a lack of motivation for the majority of farmers to invest in sensor-based intelligent systems to support and improve the technological development of their farm and the decision-making process. In this paper, a data-driven intelligent monitoring system in a cloud environment is proposed. The designed architecture enables a comprehensive solution for interaction between data extraction from IoT devices, preprocessing, storage, feature engineering, modelling, and visualization. Streaming data from IoT devices to interactive live reports along with built machine learning (ML) models are included. As a result of the proposed intelligent monitoring system, the collected data and ML modelling outcomes are visualized using a powerful dynamic dashboard. The dashboard allows users to monitor various parameters across the farm and provides an accessible way to view trends, deviations, and patterns in the data. ML models are trained on the collected data and are updated periodically. The data-driven visualization enables farmers to examine, organize, and represent collected farm’s data with the goal of better serving their needs. Performance and durability tests of the system are provided. The proposed solution is a technological bridge with which farmers can easily, affordably, and understandably monitor and track the progress of their farms with easy integration into an existing IoT system. Full article
(This article belongs to the Special Issue Ubiquitous Sensing and Intelligent Systems)
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18 pages, 25002 KiB  
Article
Visual Servoing Approach to Autonomous UAV Landing on a Moving Vehicle
by Azarakhsh Keipour, Guilherme A. S. Pereira, Rogerio Bonatti, Rohit Garg, Puru Rastogi, Geetesh Dubey and Sebastian Scherer
Sensors 2022, 22(17), 6549; https://doi.org/10.3390/s22176549 - 30 Aug 2022
Cited by 21 | Viewed by 2996
Abstract
Many aerial robotic applications require the ability to land on moving platforms, such as delivery trucks and marine research boats. We present a method to autonomously land an Unmanned Aerial Vehicle on a moving vehicle. A visual servoing controller approaches the ground vehicle [...] Read more.
Many aerial robotic applications require the ability to land on moving platforms, such as delivery trucks and marine research boats. We present a method to autonomously land an Unmanned Aerial Vehicle on a moving vehicle. A visual servoing controller approaches the ground vehicle using velocity commands calculated directly in image space. The control laws generate velocity commands in all three dimensions, eliminating the need for a separate height controller. The method has shown the ability to approach and land on the moving deck in simulation, indoor and outdoor environments, and compared to the other available methods, it has provided the fastest landing approach. Unlike many existing methods for landing on fast-moving platforms, this method does not rely on additional external setups, such as RTK, motion capture system, ground station, offboard processing, or communication with the vehicle, and it requires only the minimal set of hardware and localization sensors. The videos and source codes are also provided. Full article
(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)
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31 pages, 5515 KiB  
Article
UAV and IoT-Based Systems for the Monitoring of Industrial Facilities Using Digital Twins: Methodology, Reliability Models, and Application
by Yun Sun, Herman Fesenko, Vyacheslav Kharchenko, Luo Zhong, Ihor Kliushnikov, Oleg Illiashenko, Olga Morozova and Anatoliy Sachenko
Sensors 2022, 22(17), 6444; https://doi.org/10.3390/s22176444 - 26 Aug 2022
Cited by 20 | Viewed by 2854
Abstract
This paper suggests a methodology (conception and principles) for building two-mode monitoring systems (SMs) for industrial facilities and their adjacent territories based on the application of unmanned aerial vehicle (UAV), Internet of Things (IoT), and digital twin (DT) technologies, and a set of [...] Read more.
This paper suggests a methodology (conception and principles) for building two-mode monitoring systems (SMs) for industrial facilities and their adjacent territories based on the application of unmanned aerial vehicle (UAV), Internet of Things (IoT), and digital twin (DT) technologies, and a set of SM reliability models considering the parameters of the channels and components. The concept of building a reliable and resilient SM is proposed. For this purpose, the von Neumann paradigm for the synthesis of reliable systems from unreliable components is developed. For complex SMs of industrial facilities, the concept covers the application of various types of redundancy (structural, version, time, and space) for basic components—sensors, means of communication, processing, and presentation—in the form of DTs for decision support systems. The research results include: the methodology for the building and general structures of UAV-, IoT-, and DT-based SMs in industrial facilities as multi-level systems; reliability models for SMs considering the applied technologies and operation modes (normal and emergency); and industrial cases of SMs for manufacture and nuclear power plants. The results obtained are the basis for further development of the theory and for practical applications of SMs in industrial facilities within the framework of the implementation and improvement of Industry 4.0 principles. Full article
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15 pages, 1451 KiB  
Article
Miniterm, a Novel Virtual Sensor for Predictive Maintenance for the Industry 4.0 Era
by Eduardo Garcia, Nicolás Montés, Javier Llopis and Antonio Lacasa
Sensors 2022, 22(16), 6222; https://doi.org/10.3390/s22166222 - 19 Aug 2022
Cited by 14 | Viewed by 2277
Abstract
This article introduces a novel virtual sensor for predictive maintenance called mini-term. A mini-term can be defined as the time it takes for a part of the machine to do its job. Being a technical sub-cycle time, its function has been linked to [...] Read more.
This article introduces a novel virtual sensor for predictive maintenance called mini-term. A mini-term can be defined as the time it takes for a part of the machine to do its job. Being a technical sub-cycle time, its function has been linked to production. However, when a machine or component gets deteriorated, the mini-term also suffers deterioration, allowing it to be a multifunctional indicator for the prediction of machine failures as well as measurement of production. Currently, in Industry 4.0, one of the handicaps is Big Data and Data Analysis. However, in the case of predictive maintenance, the need to install sensors in the machines means that when the proposed scientific solutions reach the industry, they cannot be carried out massively due to the high cost this entails. The advantage introduced by the mini-term is that it can be implemented in an easy and simple way in pre-installed systems since you only need to program a timer in the PLC or PC that controls the line/machine in the production line, allowing, according to the authors’ knowledge, to build industrial Big Data on predictive maintenance for the first time, which is called Miniterm 4.0. This article shows evidence of the important improvements generated by the use of Miniterm 4.0 in a factory. At the end of the paper we show the evolution of TAV (Technical availability), Mean Time To Repair (MTTR), EM (Number of Work order (Emergency Orders/line Stop)) and OM (Labour hours in EM) showing a very important improvement as the number of mini-terms was increased and the Miniterm 4.0 system became more reliable. In particular, TAV is increased by 15%, OM is reduced in 5000 orders, MTTR is reduced in 2 h and there are produced 3000 orders less than when mini-terms did not exist. At the end of the article we discuss the benefits and limitations of the mini-terms and we show the conclusions and future works. Full article
(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques)
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13 pages, 4936 KiB  
Article
Research on the Time Drift Stability of Differential Inductive Displacement Sensors with Frequency Output
by Xiaolong Lu, Guiyun Tian, Zongwen Wang, Wentao Li, Dehua Yang, Haoran Li, You Wang, Jijun Ni and Yong Zhang
Sensors 2022, 22(16), 6234; https://doi.org/10.3390/s22166234 - 19 Aug 2022
Cited by 17 | Viewed by 2257
Abstract
An edge displacement sensor is one of the key technologies for building large segmented mirror astronomical optical telescopes. A digital interface is one novel approach for sensor technologies, digital transformation and the Internet of Things (IoT) in particular. Frequency output sensors and inductance-to-digital [...] Read more.
An edge displacement sensor is one of the key technologies for building large segmented mirror astronomical optical telescopes. A digital interface is one novel approach for sensor technologies, digital transformation and the Internet of Things (IoT) in particular. Frequency output sensors and inductance-to-digital converter (LDC) demonstrated significant advantages in comparison with conventional sensors with analog-to-digital converter (ADC) interfaces. In order for the differential inductive frequency output displacement (DIFOD) sensor to meet the high-stability requirements of segmented mirror astronomical telescopes, it is important to understand the factors for time drift of the sensor. This paper focuses on the investigation of key factors of sensor structure and material, signal conditioning and interface, and fixtures for time drift to permanently installed applications. First, the measurement principle and probe structural characteristics of the sensor are analyzed. Then, two kinds of signal conditioning and digitalization methods using resonance circuits and LDC chips are implemented and compared. Finally, the time drift stability experiments are performed on the sensors with different signal conditioning methods and fixtures under controlled temperature. Experimental results show that the magnetic shield ring effectively improves the sensitivity and quality factor of the sensors, the time drift stability of the sensor using the signal conditioning based on resonance circuits is better than that of the sensors using LDC chips, and the root mean square (RMS) of the sensor time drift meets the requirement of 0.01 μm/24 h. This study will help further development of high-stability of frequency output sensors and IoT-based systems for scaled-up applications in the future. Full article
(This article belongs to the Section Electronic Sensors)
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11 pages, 2525 KiB  
Article
Tunable Diode Laser Absorption Spectroscopy Based Temperature Measurement with a Single Diode Laser Near 1.4 μm
by Xiaonan Liu and Yufei Ma
Sensors 2022, 22(16), 6095; https://doi.org/10.3390/s22166095 - 15 Aug 2022
Cited by 60 | Viewed by 3820
Abstract
The rapidly changing and wide dynamic range of combustion temperature in scramjet engines presents a major challenge to existing test techniques. Tunable diode laser absorption spectroscopy (TDLAS) based temperature measurement has the advantages of high sensitivity, fast response, and compact structure. In this [...] Read more.
The rapidly changing and wide dynamic range of combustion temperature in scramjet engines presents a major challenge to existing test techniques. Tunable diode laser absorption spectroscopy (TDLAS) based temperature measurement has the advantages of high sensitivity, fast response, and compact structure. In this invited paper, a temperature measurement method based on the TDLAS technique with a single diode laser was demonstrated. A continuous-wave (CW), distributed feedback (DFB) diode laser with an emission wavelength near 1.4 μm was used for temperature measurement, which could cover two water vapor (H2O) absorption lines located at 7153.749 cm−1 and 7154.354 cm−1 simultaneously. The output wavelength of the diode laser was calibrated according to the two absorption peaks in the time domain. Using this strategy, the TDLAS system has the advantageous of immunization to laser wavelength shift, simple system structure, reduced cost, and increased system robustness. The line intensity of the two target absorption lines under room temperature was about one-thousandth of that under high temperature, which avoided the measuring error caused by H2O in the environment. The system was tested on a McKenna flat flame burner and a scramjet model engine, respectively. It was found that, compared to the results measured by CARS technique and theoretical calculation, this TDLAS system had less than 4% temperature error when the McKenna flat flame burner was used. When a scramjet model engine was adopted, the measured results showed that such TDLAS system had an excellent dynamic range and fast response. The TDLAS system reported here could be used in real engine in the future. Full article
(This article belongs to the Special Issue State-of-the-Art Optical Sensors Technology in China)
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20 pages, 11596 KiB  
Article
Electroactive Biofilms of Activated Sludge Microorganisms on a Nanostructured Surface as the Basis for a Highly Sensitive Biochemical Oxygen Demand Biosensor
by Saniyat Kurbanalieva, Vyacheslav Arlyapov, Anna Kharkova, Roman Perchikov, Olga Kamanina, Pavel Melnikov, Nadezhda Popova, Andrey Machulin, Sergey Tarasov, Evgeniya Saverina, Anatoly Vereshchagin and Anatoly Reshetilov
Sensors 2022, 22(16), 6049; https://doi.org/10.3390/s22166049 - 12 Aug 2022
Cited by 11 | Viewed by 2771
Abstract
The possibility of the developing a biochemical oxygen demand (BOD) biosensor based on electroactive biofilms of activated sludge grown on the surface of a graphite-paste electrode modified with carbon nanotubes was studied. A complex of microscopic methods controlled biofilm formation: optical microscopy with [...] Read more.
The possibility of the developing a biochemical oxygen demand (BOD) biosensor based on electroactive biofilms of activated sludge grown on the surface of a graphite-paste electrode modified with carbon nanotubes was studied. A complex of microscopic methods controlled biofilm formation: optical microscopy with phase contrast, scanning electron microscopy, and laser confocal microscopy. The features of charge transfer in the obtained electroactive biofilms were studied using the methods of cyclic voltammetry and electrochemical impedance spectroscopy. The rate constant of the interaction of microorganisms with the extracellular electron carrier (0.79 ± 0.03 dm3(g s)−1) and the heterogeneous rate constant of electron transfer (0.34 ± 0.02 cm s−1) were determined using the cyclic voltammetry method. These results revealed that the modification of the carbon nanotubes’ (CNT) electrode surface makes it possible to create electroactive biofilms. An analysis of the metrological and analytical characteristics of the created biosensors showed that the lower limit of the biosensor based on an electroactive biofilm of activated sludge is 0.41 mgO2/dm3, which makes it possible to analyze almost any water sample. Analysis of 12 surface water samples showed a high correlation (R2 = 0.99) with the results of the standard method for determining biochemical oxygen demand. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2022)
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33 pages, 19332 KiB  
Article
Smart Strawberry Farming Using Edge Computing and IoT
by Mateus Cruz, Samuel Mafra, Eduardo Teixeira and Felipe Figueiredo
Sensors 2022, 22(15), 5866; https://doi.org/10.3390/s22155866 - 5 Aug 2022
Cited by 24 | Viewed by 5907
Abstract
Strawberries are sensitive fruits that are afflicted by various pests and diseases. Therefore, there is an intense use of agrochemicals and pesticides during production. Due to their sensitivity, temperatures or humidity at extreme levels can cause various damages to the plantation and to [...] Read more.
Strawberries are sensitive fruits that are afflicted by various pests and diseases. Therefore, there is an intense use of agrochemicals and pesticides during production. Due to their sensitivity, temperatures or humidity at extreme levels can cause various damages to the plantation and to the quality of the fruit. To mitigate the problem, this study developed an edge technology capable of handling the collection, analysis, prediction, and detection of heterogeneous data in strawberry farming. The proposed IoT platform integrates various monitoring services into one common platform for digital farming. The system connects and manages Internet of Things (IoT) devices to analyze environmental and crop information. In addition, a computer vision model using Yolo v5 architecture searches for seven of the most common strawberry diseases in real time. This model supports efficient disease detection with 92% accuracy. Moreover, the system supports LoRa communication for transmitting data between the nodes at long distances. In addition, the IoT platform integrates machine learning capabilities for capturing outliers in collected data, ensuring reliable information for the user. All these technologies are unified to mitigate the disease problem and the environmental damage on the plantation. The proposed system is verified through implementation and tested on a strawberry farm, where the capabilities were analyzed and assessed. Full article
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33 pages, 17395 KiB  
Article
Intelligent Smart Marine Autonomous Surface Ship Decision System Based on Improved PPO Algorithm
by Wei Guan, Zhewen Cui and Xianku Zhang
Sensors 2022, 22(15), 5732; https://doi.org/10.3390/s22155732 - 31 Jul 2022
Cited by 13 | Viewed by 2884
Abstract
With the development of artificial intelligence technology, the behavior decision-making of an intelligent smart marine autonomous surface ship (SMASS) has become particularly important. This research proposed local path planning and a behavior decision-making approach based on improved Proximal Policy Optimization (PPO), which could [...] Read more.
With the development of artificial intelligence technology, the behavior decision-making of an intelligent smart marine autonomous surface ship (SMASS) has become particularly important. This research proposed local path planning and a behavior decision-making approach based on improved Proximal Policy Optimization (PPO), which could drive an unmanned SMASS to the target without requiring any human experiences. In addition, a generalized advantage estimation was added to the loss function of the PPO algorithm, which allowed baselines in PPO algorithms to be self-adjusted. At first, the SMASS was modeled with the Nomoto model in a simulation waterway. Then, distances, obstacles, and prohibited areas were regularized as rewards or punishments, which were used to judge the performance and manipulation decisions of the vessel Subsequently, improved PPO was introduced to learn the action–reward model, and the neural network model after training was used to manipulate the SMASS’s movement. To achieve higher reward values, the SMASS could find an appropriate path or navigation strategy by itself. After a sufficient number of rounds of training, a convincing path and manipulation strategies would likely be produced. Compared with the proposed approach of the existing methods, this approach is more effective in self-learning and continuous optimization and thus closer to human manipulation. Full article
(This article belongs to the Special Issue Mobile Robots: Navigation, Control and Sensing)
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23 pages, 3338 KiB  
Article
Design, Implementation and Experimental Investigation of a Pedestrian Street Crossing Assistance System Based on Visible Light Communications
by Alin-Mihai Căilean, Cătălin Beguni, Sebastian-Andrei Avătămăniței, Mihai Dimian and Valentin Popa
Sensors 2022, 22(15), 5481; https://doi.org/10.3390/s22155481 - 22 Jul 2022
Cited by 13 | Viewed by 4016
Abstract
In urban areas, pedestrians are the road users category that is the most exposed to road accident fatalities. In this context, the present article proposes a totally new architecture, which aims to increase the safety of pedestrians on the crosswalk. The first component [...] Read more.
In urban areas, pedestrians are the road users category that is the most exposed to road accident fatalities. In this context, the present article proposes a totally new architecture, which aims to increase the safety of pedestrians on the crosswalk. The first component of the design is a pedestrian detection system, which identifies the user’s presence in the region of the crosswalk and determines the future street crossing action possibility or the presence of a pedestrian engaged in street crossing. The second component of the system is the visible light communications part, which is used to transmit this information toward the approaching vehicles. The proposed architecture has been implemented at a regular scale and experimentally evaluated in outdoor conditions. The experimental results showed a 100% overall pedestrian detection rate. On the other hand, the VLC system showed a communication distance between 5 and 40 m when using a standard LED light crosswalk sign as a VLC emitter, while maintaining a bit error ratio between 10−7 and 10−5. These results demonstrate the fact that the VLC technology is now able to be used in real applications, making the transition from a high potential technology to a confirmed technology. As far as we know, this is the first article presenting such a pedestrian street crossing assistance system. Full article
(This article belongs to the Special Issue Automotive Visible Light Communications (AutoVLC))
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