3D Sensor Research Articles

Application of Poisson’s ratio structures and decoupling algorithm for 3D force sensing

Flexible tactile electronic devices are extensively used in the fields of robotics, medical detection, and human-computer interaction. Monitoring contact parameters, including force magnitude, direction, and contact location, is particularly vital for skin-like tactile sensing devices. Herein, a 3D force sensor is designed based on porous structure with deliberately designed Poisson’s ratios. A genetic algorithm (GA) optimized back propagation neuronal network (BPNN) model is proposed to support the 3D force decoupling, which can greatly improve the decoupling accuracy. The introduction of the GA-BPNN significantly enhances decoupling accuracy compared to the initial neural network. Micro-porous structures with varied Poisson’s ratios are embedded into the sensing unit to achieve better sensibility. Significantly, this study underscores that the decoupling accuracy of the force components along the Z-axis can be further improved by substituting the solid unit with a designed porous structure unit featuring a specific Poisson’s ratio in an arrayed 3D force sensor.

Fault Signal Perception of Nanofiber Sensor for 3D Human Motion Detection Using Multi-Task Deep Learning

Once a fault occurs in the nanofiber sensor, the scientific and reliable three-dimensional (3D) human motion detection results will be compromised. It is necessary to accurately and rapidly perceive the fault signals of the nanofiber sensor and determine the type of fault, to enable it to continue operating in a sustained and stable manner. Therefore, we propose a fault signal perception method for 3D human motion detection nanofiber sensor based on multi-task deep learning. First, through obtaining the fault characteristic parameters of the nanofiber sensor, the fault of the nanofiber sensor is reconstructed to complete the fault location of the nanofiber sensor. Second, the fault signal of the nanofiber sensor is mapped by the penalty function, and the feature extraction model of the fault signal of the nanofiber sensor is constructed by combining the multi-task deep learning. Finally, the multi-task deep learning algorithm is used to calculate the sampling frequency of the fault signal, and the key variable information of the fault of the nanofiber sensor is extracted according to the amplitude of the state change of the nanofiber sensor, to realize the perception of the fault signal of the nanofiber sensor. The results show that the proposed method can accurately perceive the fault signal of a nanofiber sensor in 3D human motion detection, the maximum sensor fault location accuracy is 97%, and the maximum noise content of the fault signal is only 5 dB, which shows that the method can be widely used in fault signal perception.

Robot Indoor Navigation: Comparative Analysis of LiDAR 2D and Visual SLAM

<span lang="EN-US">Robot indoor navigation has become a significant area of research and development for applications such as autonomous robots, smart homes, and industrial automation. This article presents an in-depth comparative analysis of LiDAR 2D and visual sensor simultaneous localization and mapping (SLAM) approaches for robot indoor navigation. The increasing demand for autonomous robots in indoor environments has led to the development of various SLAM techniques for mapping and localization. LiDAR 2D and visual sensor-based SLAM methods are widely used due to their low cost and ease of implementation. The article provides an overview of LiDAR 2D and visual sensor-based SLAM techniques, including their working principles, advantages, and limitations. A comprehensive comparative analysis is conducted, assesing their capabilities in terms of robustness, accuracy, and computational requirements. The article also discusses the impact of environmental factors, such as lighting conditions and obstacles, on the performance of both approaches. The analysis’s findings highlight each approach’s strengths and weaknesses, providing valuable insights for researchers and practitioners in selecting the appropriate SLAM method for robot indoor navigation based on specific requirements and constraints</span>

OFDR shape sensor based on a femtosecond-laser-inscribed weak fiber Bragg grating array in a multicore fiber.

An optical frequency domain reflectometry (OFDR) shape sensor was demonstrated based on a femtosecond-laser-inscribed weak fiber Bragg grating (WFBG) array in a multicore fiber (MCF). A WFBG array consisting of 60 identical WFBGs was successfully inscribed in each core along a 60 cm long MCF using the femtosecond-laser point-by-point technology, where the length and space of each WFBG were 2 and 8 mm, respectively. The strain distribution of each core in two-dimensional (2D) and three-dimensional (3D) shape sensing was successfully demodulated using the traditional cross correlation algorithm, attributed to the accurate localization of each WFBG. The minimum reconstruction error per unit length of the 2D and 3D shape sensors has been improved to 1.08% and 1.07%, respectively, using the apparent curvature vector method based on the Bishop frame.

High-conductivity and long-term stability strain sensor based on silk fibroin and polyvinyl alcohol hydrogels

Conductive hydrogels as promising candidates of wearable electronic have attracted considerable interest in health monitoring, multifunctional electronic skins, and human-machine interfaces. However, to simultaneously achieve excellent electrical properties, long-term stability, and water vapor permeability of conductive hydrogels remains a challenge. Herein, a highly conductive hydrogel (MPRS) with high breathability (5.90 mg∙cm−2∙h−1), excellent electrical conductivity (6.67 S/m), high elongation (315%), and remarkable water retention properties was prepared adopting cold freeze-thawing method. MXene, poly (3, 4-ethylenedioxythiophene): poly (styrenesulfonate) (PEDOT: PSS), and reduced graphene oxide were used as conductive materials, while silk fibroin (SF) and polyvinyl alcohol (PVA) were used as the gel matrix. Lithium chloride solution was used as a solvent during the preparation process. The strain sensor based on this hydrogel demonstrates rapid response (0.33 s) and recovery ability (0.31 s) to strain stimuli. Upon 250 cycles of stretching at 35% strain, the output signal remains stable and highly repeatable, ensuring accurate detection of human joint movements and facial expressions. The fabrication of flexible electrodes using the hydrogel enabled real-time monitoring of human electromyography and electrocardiogram signals. The applications in a 2D sensor array and human-machine interaction interfaces also proved the excellent performance of this conductive hydrogels in wearable sensor.

Disposable Stochastic Miniplatforms for Simultaneous Recognition and Determination of Vitamins B5, B7, and B9 in Food and Pharmaceutical Compounds.

Vitamins are essential for supporting daily physiological functions and are crucial for metabolic processes, including preventing vascular events and delaying the progression of diabetic nephropathy. The specific assessment of food and pharmaceutical compound quality requires the highly sensitive evaluation of vitamins B5, B7, and B9. Therefore, it is necessary to have a fast screening method for multivitamin tablets, pharmaceutical tablets, water, food, including raw egg yolk, and biological fluids, including urine, in order to ensure the precise identification and measurement of vitamins B5, B7, and B9. This paper introduces a novel disposable stochastic miniplatform that utilizes an incorporated 2D stochastic sensor to be able to simultaneously assay the three vitamins. Cobalt-phthalocyanine was used to modify the carbon film of the 2D sensor. High sensitivities (of 102-108 s-1 g-1 mL magnitude order) and low detection limits (of pg mL-1 magnitude order) were recorded for the disposable stochastic miniplatforms. The validation procedure involved the utilization of pharmaceutical tablets, supplement tablets, water samples, food samples, including raw egg yolk, and biological samples, specifically urine. The disposable stochastic miniplatforms demonstrate cost-effectiveness and suitability for the fast screening tests, with a time frame of 6 min, across a range of samples, a capacity of over 150 measurements, and an endurance of up to one month. Recovery values higher than 94.00% with RSD (%) values lower than 1.00 were recorded.

Proportional control of a parallel robogami delta mechanism with embedded 2D printed angle sensor feedback

Robogami structures are employed for many purposes, including miniature parallel manipulators. Parallel manipulators require accurate joint position feedback, underlining the open problem in robogami structures for providing sophisticated feedback with 2D, fully embeddable, down-scalable, flexible sensors. Existing studies include only on-off control applications, excluding any parallel mechanism implementations and continuous proportional control signals. We implement silver inkjet printed angle sensors for the first time into a robogami structure and use this class of sensors for control. For the first time, a parallel manipulator is controlled with a 2D sensor, and printed sensors are tested for continuous control. Performances are analyzed with set-point and tracking control tasks using a paper Delta mechanism, compared with on-off control and encoder feedback control cases for better evaluation. A detailed comparison with the existing studies both for robogami parallel mechanisms and other sensor-embedded flexible hinges is provided. Encoder-like control performances are achieved in a wide range of the joint space and down to ~1 mm. RMS task space accuracy is recorded. Sources and potential improvements for the less accurate regions are stated. We conclude that printed sensors are promising for parallel mechanisms and continuous signal control applications.

3D power frequency intensive electric field sensor based on a lithium niobate electro-optic crystal.

In order to conveniently measure the power frequency intensive electric field in any direction in space, a lithium niobate (LN) crystal-based three dimensional (3D) intensive electric field sensor with an all-dielectric structure has been developed. A regular triangular prism structure with three 1D (one-dimensional) electric field sensors fixed on its three sides is designed and fabricated. By setting the orientation of the z-axes of the three 1D electric field sensors at 54.7° with respect to the three edges of the prism, the detection directions of the three 1D electric field sensors are perpendicular to each other. Compared to existing 3D electric field sensors that directly arrange three 1D electric field sensors along three mutually perpendicular directions, the overall device volume of the newly designed 3D sensor is significantly reduced. Experimental results reveal that the linear measurement ranges of the 3D electric field sensor along the three axes are 3.71kV/m-388kV/m, 2.78kV/m-403kV/m, and 4.50kV/m-375kV/m, respectively. When the electric field strength is 82.05kV/m, the measurement error is less than 4.97% when the 3D electric field sensor is rotated 360° in the y o z and x o y planes.

Far-Field Spatial Response of Off-Diagonal GMI Wire Magnetometers. Application to Magnetic Field Sources Sensing

Studying the spatial response of a single-axis magnetometer could be the key parameter to optimize the ultimate performances of magnetic heads of detection. Indeed, the problem of non-orthogonality, misalignment, and 3D spatial response could be improved based on the knowledge of the 3D sensor spatial response. In that way, we have investigated the latter for our giant magneto-impedance (GMI) magnetometer, as a far-field pattern, by using a three-axis Helmholtz coil system. Firstly, we calibrate our device and secondly, we apply a specific 3D magnetic field to obtain this pattern. The latter helps to observe the directional or angular dependence of the sensor sensitivity versus the applied magnetic field, as we exemplified. The results confirm the excellent directivity of our off-diagonal GMI magnetometer. The evaluation of the associated error compared to an ideal vector magnetometer is also given and discussed.

Efficacy of microchips and 3D sensors for orthodontic force measurement: A systematic review of invitro studies.

To evaluate the efficacy of microchips and 3D microsensors in the measurement of orthodontic forces. Through September 2023, comprehensive searches were conducted on PubMed/MEDLINE, SCOPUS and SCIELO without restrictions. After removing duplicate entries and applying the eligibility criteria, 23 studies were included for analysis. All the studies were conducted invitro, and slightly more than half of them were centred on evaluating orthodontic forces exerted by aligners. Eight utilized microchips as measurement tools, while the remaining studies made use of 3D microsensors for their assessments. In the context of fixed appliances, key findings included a high level of agreement in 3-dimensional orthodontic force detection between simulation results and actual applied forces. Incorporating critical force-moment combinations during smart bracket calibration reduced measurement errors for most components. Translational tooth movement revealed a moment-to-force ratio, aligning with the bracket's centre of resistance. The primary findings in relation to aligners revealed several significant factors affecting the forces exerted by them. Notably, the foil thickness and staging were found to have a considerable impact on these forces, with optimal force transmission occurring at a layer height of 150 μm. Furthermore, the type of material used in 3D-printing aligners influenced the force levels, with attachments proving effective in generating extrusive forces. Deliberate adjustments in aligner thickness were observed to alter the forces and moments generated. Microchips and 3D sensors provide precise and quantitative measurements of orthodontic forces in invitro studies, enabling accurate monitoring and control of tooth movement.

Research on Three-dimensional Reconstruction

Nowadays, with the continuous development of the field of computer vision and computer graphics, 3D reconstruction technology has become an area that has attracted much of people’s attention. 3D reconstruction refers to creating a 3D model of an object in the real world by extracting information from multiple 2D images or sensor data. This technology has important usages in many fields, including virtual reality, augmented reality, cultural heritage preservation, medical image processing, industrial design, etc. This thesis examines the application of 3D reconstruction in various fields, discusses the definition of development history, its wide application in life, and its advantages and disadvantages. This article is divided into four parts and discusses the algorithm research of 3D reconstruction in four fields. Through an in-depth study of 3D reconstruction technology, we can better understand its role in different fields and provide strong support for developing and innovating related applications. As more accurate devices and algorithms continue to advance, 3D reconstruction technology is expected to show its significant potential in more and more fields and create more realistic and useful digital experiences for us.

The surface edge explorer (SEE): A measurement-direct approach to next best view planning

High-quality observations of the real world are crucial for a variety of applications, including producing 3D printed replicas of small-scale scenes and conducting inspections of large-scale infrastructure. These 3D observations are commonly obtained by combining multiple sensor measurements from different views. Guiding the selection of suitable views is known as the Next Best View (NBV) planning problem. Most NBV approaches reason about measurements using rigid data structures (e.g., surface meshes or voxel grids). This simplifies next best view selection but can be computationally expensive, reduces real-world fidelity and couples the selection of a next best view with the final data processing. This paper presents the Surface Edge Explorer (SEE), a NBV approach that selects new observations directly from previous sensor measurements without requiring rigid data structures. SEE uses measurement density to propose next best views that increase coverage of insufficiently observed surfaces while avoiding potential occlusions. Statistical results from simulated experiments show that SEE can attain similar or better surface coverage with less observation time and travel distance than evaluated volumetric approaches on both small- and large-scale scenes. Real-world experiments demonstrate SEE autonomously observing a deer statue using a 3D sensor affixed to a robotic arm.

Integrated Sandwich-Paper 3D Cell Sensing Device to In Situ Wirelessly Monitor H2O2 Released from Living Cells.

Point-of-care testing (POCT) has attracted great interest because of its prominent advantages of rapidness, precision, portability, and real-time monitoring, thus becoming a powerful biomedical device in early clinical diagnosis and convenient medical treatments. However, its complicated manufacturing process and high expense severely impede mass production and broad applications. Herein, an innovative but inexpensive integrated sandwich-paper three-dimensional (3D) cell sensing device is fabricated to in situ wirelessly detect H2O2 released from living cells. The paper-based electrochemical sensing device was constructed by a sealed sandwiched bottom plastic film/fiber paper/top hole-centered plastic film that was printed with patterned electrodes. A new (Fe, Mn)3(PO4)2/N-doped carbon nanorod was developed and immobilized on the sensing carbon electrode while cell culture solution filled the exposed fiber paper, allowing living cells to grow on the fiber paper surrounding the electrode. Due to the significantly shortening diffusion distance to access the sensing sites by such a unique device and a rationally tuned ratio of Fe2+/Mn2+, the device exhibits a fast response time (0.2 s), a low detection limit (0.4 μM), and a wide detection range (2-3200 μM). This work offers great promise for a low-cost and highly sensitive POCT device for practical clinic diagnosis and broad POCT biomedical applications.

Utilizing 3D Point Cloud Technology with Deep Learning for Automated Measurement and Analysis of Dairy Cows.

This paper introduces an approach to the automated measurement and analysis of dairy cows using 3D point cloud technology. The integration of advanced sensing techniques enables the collection of non-intrusive, precise data, facilitating comprehensive monitoring of key parameters related to the health, well-being, and productivity of dairy cows. The proposed system employs 3D imaging sensors to capture detailed information about various parts of dairy cows, generating accurate, high-resolution point clouds. A robust automated algorithm has been developed to process these point clouds and extract relevant metrics such as dairy cow stature height, rump width, rump angle, and front teat length. Based on the measured data combined with expert assessments of dairy cows, the quality indices of dairy cows are automatically evaluated and extracted. By leveraging this technology, dairy farmers can gain real-time insights into the health status of individual cows and the overall herd. Additionally, the automated analysis facilitates efficient management practices and optimizes feeding strategies and resource allocation. The results of field trials and validation studies demonstrate the effectiveness and reliability of the automated 3D point cloud approach in dairy farm environments. The errors between manually measured values of dairy cow height, rump angle, and front teat length, and those calculated by the auto-measurement algorithm were within 0.7 cm, with no observed exceedance of errors in comparison to manual measurements. This research contributes to the burgeoning field of precision livestock farming, offering a technological solution that not only enhances productivity but also aligns with contemporary standards for sustainable and ethical animal husbandry practices.

3D Flexible Wind Sensor With its Optimization and Environmental Effect

3D Flexible Wind Sensor With its Optimization and Environmental Effect

Bioinspired Whisker Sensor for 3D Mapping of Underground Mining Environments.

Traversing through challenging, unstructured environments, particularly in mining scenarios characterized by dust concentration, darkness, and lack of communication presents formidable obstacles for traditional sensing technologies. Drawing inspiration from naked mole rats, characterized as being skilled subterranean navigators that depend heavily on touch to navigate their environment, this study introduces a new whisker-sensing disk designed for 3D mapping in unstructured environments. The disk comprises a circular array of 32 whisker sensors, each featuring a slender flexible plastic rod attached to a compliant base housing a 3D Hall-effect sensor. The whisker sensor is modeled using both analytical and data-driven approaches to predict rotation angles based on magnetic field measurements. The validation and comparison of both models are performed by evaluating data from other whisker sensors. Additionally, a series of experiments demonstrates the whisker disk's capability in performing 3D-mapping tasks, along with successful implementation on diverse robotic platforms, highlighting its future potential for effective 3D mapping in complex and unstructured subterranean environments.

A Novel Approach to Light Detection and Ranging Sensor Placement for Autonomous Driving Vehicles Using Deep Deterministic Policy Gradient Algorithm

<div>This article presents a novel approach to optimize the placement of light detection and ranging (LiDAR) sensors in autonomous driving vehicles using machine learning. As autonomous driving technology advances, LiDAR sensors play a crucial role in providing accurate collision data for environmental perception. The proposed method employs the deep deterministic policy gradient (DDPG) algorithm, which takes the vehicle’s surface geometry as input and generates optimized 3D sensor positions with predicted high visibility. Through extensive experiments on various vehicle shapes and a rectangular cuboid, the effectiveness and adaptability of the proposed method are demonstrated. Importantly, the trained network can efficiently evaluate new vehicle shapes without the need for re-optimization, representing a significant improvement over classical methods such as genetic algorithms. By leveraging machine learning techniques, this research streamlines the sensor placement optimization process, enhancing the perception capabilities of autonomous driving vehicles. The optimized sensor configurations obtained from the DDPG algorithm lead to safer and more reliable autonomous driving systems, contributing to the advancement and widespread adoption of autonomous driving technology.</div>

Soft, full Wheatstone bridge 3D pressure sensors for cardiovascular monitoring

Variations in parameters associated with the ambient environment can introduce noise in soft, body-worn sensors. For example, many piezoresistive pressure sensors exhibit a high degree of sensitivity to fluctuations in temperature, thereby requiring active compensation strategies. The research presented here addresses this challenge with a multilayered 3D microsystem design that integrates four piezoresistive sensors in a full-Wheatstone bridge configuration. An optimized layout of the sensors relative to the neutral mechanical plane leads to both an insensitivity to temperature and an increased sensitivity to pressure, relative to previously reported devices that rely on similar operating principles. Integrating this 3D pressure sensor into a soft, flexible electronics platform yields a system capable of real-time, wireless measurements from the surface of the skin. Placement above the radial and carotid arteries yields high-quality waveforms associated with pulsatile blood flow, with quantitative correlations to blood pressure. The results establish the materials and engineering aspects of a technology with broad potential in remote health monitoring.

An autonomous Unmanned Aerial Vehicle exploration platform with a hierarchical control method for post‐disaster infrastructures

AbstractCatastrophic natural disasters like earthquakes can cause infrastructure damage. Emergency response agencies need to assess damage precisely while repeating this process for infrastructures with different shapes and types. The authors aim for an autonomous Unmanned Aerial Vehicle (UAV) platform equipped with a 3D LiDAR sensor to comprehensively and accurately scan the infrastructure and map it with a predefined resolution r. During the inspection, the UAV needs to decide on the Next Best View (NBV) position to maximize the gathered information while avoiding collision at high speed. The authors propose solving this problem by implementing a hierarchical closed‐loop control system consisting of a global planner and a local planner. The global NBV planner decides the general UAV direction based on a history of measurements from the LiDAR sensor, and the local planner considers the UAV dynamics and enables the UAV to fly at high speed with the latest LiDAR measurements. The proposed system is validated through the Regional Scale Autonomous Swarm Damage Assessment simulator, which is built by the authors. Through extensive testing in three unique and highly constrained infrastructure environments, the autonomous UAV inspection system successfully explored and mapped the infrastructures, demonstrating its versatility and applicability across various shapes of infrastructure.

3D printing wide detection range and high sensitivity flexible pressure sensor and its applications

Piezoresistive flexible pressure sensors hold significant promise in various applications, including electronic skin, human-machine interaction, and health monitoring. Nevertheless, conventional piezoresistive flexible pressure sensors encounter challenges in achieving both high sensitivity and wide detection range, which remain unresolved. The design of a sensor's microstructure presents itself as an effective strategy for enhancing sensor performance. Microporous structure is a commonly employed microstructure that substantially enhances the sensor's detection range. However, flexible pressure sensors with microporous structures often exhibit low sensitivity which limits their applications. Consequently, this study introduces an innovative layered and microporous structure to upgrade flexible pressure sensor with high sensitivity as well as wide detection range. By integrating 3D direct writing printing technology with foaming materials, multi-walled carbon nanotubes are incorporated as conductive fillers to fabricate a piezoresistive flexible pressure sensor (PPS) featuring both microporous and 3D structures. This sensor exhibits high sensitivity (0.298 kPa−1), a wide detection range (25 Pa to 130 kPa), favorable response time, and durability, which can be applicable to robotic arms, smart gloves, and other devices requiring effective stress detection. This study unveils novel design concepts for a category of piezoresistive flexible pressure sensors that are compatible with high sensitivity and wide detection range requirements.