Composite robots often encounter difficulties due to changes in illumination, external disturbances, reflective surface effects, and cumulative errors. These challenges significantly hinder their capabilities in envir...
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Composite robots often encounter difficulties due to changes in illumination, external disturbances, reflective surface effects, and cumulative errors. These challenges significantly hinder their capabilities in environmental perception and the accuracy and reliability of pose estimation. We propose a nonlinear optimization approach to overcome these issues to develop an integrated localization and navigation framework, IIVL-LM (IMU, Infrared, Vision, and LiDAR Fusion for Localization and Mapping). This framework achieves tightly coupled integration at the data level using inputs from an IMU (Inertial Measurement Unit), an infrared camera, an RGB (Red, Green and Blue) camera, and LiDAR. We propose a real-time luminance calculation model and verify its conversion accuracy. Additionally, we designed a fast approximation method for the nonlinear weighted fusion of features from infrared and RGB frames based on luminance values. Finally, we optimize the VIO (Visual-Inertial Odometry) module in the R3LIVE++ (Robust, Real-time, Radiance Reconstruction with LiDAR-Inertial-Visual state Estimation) framework based on the infrared camera's capability to acquire depth information. In a controlled study, using a simulated indoor rescue scenario dataset, the IIVL-LM system demonstrated significant performance enhancements in challenging luminance conditions, particularly in low-light environments. Specifically, the average RMSE ATE (Root Mean Square Error of absolute trajectory Error) improved by 23% to 39%, with reductions from 0.006 to 0.013. At the same time, we conducted comparative experiments using the publicly available TUM-VI (Technical University of Munich Visual-Inertial Dataset) without the infrared image input. It was found that no leading results were achieved, which verifies the importance of infrared image fusion. By maintaining the active engagement of at least three sensors at all times, the IIVL-LM system significantly boosts its robustness in both unknown and
As new production system concepts emerge to face an increasing demand for individualized production, sensorintegration for Industry 4.0 functions is becoming a major challenge. Flexibility- and scalability-focused co...
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As new production system concepts emerge to face an increasing demand for individualized production, sensorintegration for Industry 4.0 functions is becoming a major challenge. Flexibility- and scalability-focused concepts rely on reconfigurable machines that automatically replace smart end effectors between production steps. To ensure adaptability to future demands, these smart components, comprising the main tool and sensors, are connected to the static part of the machine via a unified electro-mechanical interface. In robot-based concepts, these components are furthermore space- and weight-constrained and should be cost-optimized. With respect to sensorintegration, this leads to the challenge of interfacing sensors using different communication protocols and electrical signal properties through a fixed, minimal set of signal lines. In this paper, an architecture for an adaptive sensorintegration unit is presented, targeting highly flexible production systems. Leveraging dynamic partial FPGA reconfiguration to exchange communication logic and an extensible hardware module located in the static part of the machine, it efficiently supports alternating communication protocols over static signal lines. It thereby reduces the number of signal lines as well as the need for protocol conversion units in the exchangeable components. A prototype implementation using industrial bus protocols shows its suitability and is evaluated concerning relevant timing characteristics and resource usage.
Plasmonic sensors utilizing surface plasmon resonance have become powerful tools for sensitive and label-free detection across diverse fields. The dual-core silver-coated plasmonic sensor is essential for its enhanced...
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We proposed the fabrication of the laminated wafer with the conductive diamond layer as an alternative SOI wafer for micro-electro mechanical systems (MEMS) sensors by chemical vapor deposition (CVD) and surface-activ...
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ISBN:
(纸本)9781665405676
We proposed the fabrication of the laminated wafer with the conductive diamond layer as an alternative SOI wafer for micro-electro mechanical systems (MEMS) sensors by chemical vapor deposition (CVD) and surface-activated bonding (SAB), in order to prevent the charge-up of devices on a BOX layer during plasma treatments. We demonstrated that the use of this laminated wafer could be deposited the boron-doped diamond layer and be bonded a silicon wafer (layer) at room temperature without a thermal stress. Therefore, we believe that this laminated wafer can contribute to the improvement of MEMS sensors as an alternative SOI wafer.
Flexible and wearable strain sensors herald a new era of technological innovations. The utilization of laser irradiation techniques creates conductive pathways to form graphene on a stretchable platform has gained wid...
Flexible and wearable strain sensors herald a new era of technological innovations. The utilization of laser irradiation techniques creates conductive pathways to form graphene on a stretchable platform has gained widespread traction. Herein, this paper reports the development of a LIG/Kevlar textile-based strain sensor through direct laser patterning. Applying the optimal laser scribing parameters produces high-quality graphene with a sheet resistance as low as 2.36 Ω/sq. The fabricated strain sensor demonstrates excellent sensitivity (GF max = 3215) within a narrow strain range (strain below 10%), an ultrashort response time of 20 ms, and a recovery time of 30 ms. These remarkable attributes enable the integration of graphene onto a wearable glove, unlocking its potential for human finger monitoring.
Nowadays, flexible hydrogels with electrical conductivity have become extremely helpful with broad application scenarios in electronic membranes in human-machine boundaries. However, a design approach to adjust and am...
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Nowadays, flexible hydrogels with electrical conductivity have become extremely helpful with broad application scenarios in electronic membranes in human-machine boundaries. However, a design approach to adjust and ameliorate conductivity, mechanical strength, transparency, self-healing, and good adhesion remains a challenge. One of these strategies is mussels-mimicking adhesive hydrogels, presenting strong adhesion. Here, we aim to meet the challenge by pursuing a mussel-inspired design approach, which leads to the formation of a transparent, self-healing, adhesive, and electrically conductive hydrogel from dopamine (DA), pyrrole (Py), acrylamide (AM), functionalized iron, and copper ions as an advanced electronic sensor. The transparent hydrogels were prepared using a sonication polymerization method. The results have shown that hydrogels containing copper or iron have conductivity (12 x 10-4 or 27 x 10-4 S/cm), mechanical strength (11.2 or 18.4 kPa), elongation at break (EB: 652.231% or 660.198%) and gauge factors (GF: 3.04679, 2.93513). Also, hydrogels can maintain their conductivity if they are twisted or stretched. Self-healing behavior, swelling, and water-induced shape memory effect (SME) due to hydrogen bonds and pi-pi interactions were observed. The behavior of this hydrogel as wearable sensors in integration with different body tissues such as finger, wrist, breathing, and frown was investigated. The obtained hydrogels showed high sensitivity in different areas of the body, fast response time in the strain range of 0%-%500, and high adhesion and transparency. Development of a mussel inspired flexible hydrogel. image
More interest and attention have been given to the inductive angular position sensor because of its high accuracy, simplified integration and low price in the electric drive system. Three models with different charact...
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In the last years, the manufacturing of smart textile (e-textile) has been showing outstanding achievements in the fabrication of electronic systems, thanks to their exceptional electrical, thermal and optical propert...
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ISBN:
(数字)9798350383263
ISBN:
(纸本)9798350383270
In the last years, the manufacturing of smart textile (e-textile) has been showing outstanding achievements in the fabrication of electronic systems, thanks to their exceptional electrical, thermal and optical properties. However, material performance and sustainability, complex integration methods and limited end-of-life processability are major challenges to wide adoption of e-textiles. In this work, a commercial 100% Nettle-based fabric coated with a Gelatin-glycerol resin, acting as planaritazion layer, was used for the first time as a substrate for the fabrication of thin-film temperature sensors. The devices were tested and characterized over a temperature range from 25 °C to 60 °C (at relative humidity of 26 ± 3%), showing a temperature coefficient of resistance α of 2.3×10
−3
°C
−1
. Device functionality was demonstrated down to 2mm bending radius. In addition, the temperature sensor showed degradation properties, since natural dissolution occurred within 10 days, while it took 14 days for the planarization layer to degrade in De-Ionized (DI) water. The development of this device represents the first demonstration of a sustainable and fully-green technology for the fabrication of thin-film temperature sensors directly on nature-based fabrics, with possible application in recyclable wearable electronics and human health monitoring.
The goal of this article is to provide a novel and creative performance experience by developing a music structure and beat recognition model, designing a social entertainment robot music performance system, and achie...
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The goal of this article is to provide a novel and creative performance experience by developing a music structure and beat recognition model, designing a social entertainment robot music performance system, and achieving collaborative music performance between robots and humans. Research on using edge detection algorithms to partition music structures and determine the beat and rhythm changes of music. Using machine learning algorithms to recognize and classify music beats to adapt to different music styles and rhythms. By utilizing Generative Adversarial Networks (GANs), dance segments that conform to musical beats and rhythms were generated and combined with musical performances. In order to ensure the accuracy and smoothness of music performance, various sensors were integrated to monitor the robot's posture and motion status in real-time. After experimental verification, by integrating technologies such as music recognition, dance generation, and sensor data control, collaborative performance between robots and humans can be achieved. Research has shown that close collaboration between robots and performers can be achieved, resulting in collaborative performance. This not only improves the quality and effectiveness of music performance, but also increases entertainment and interactivity, bringing users a richer and more interesting music entertainment experience.
Wearable sensors play an important role in the field of human health status monitoring and evaluation as well as rehabilitation training. Conventional flexible sensors require additional communication modules. In this...
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ISBN:
(纸本)9781665431538
Wearable sensors play an important role in the field of human health status monitoring and evaluation as well as rehabilitation training. Conventional flexible sensors require additional communication modules. In this work, we proposed a flexible antenna-derived sensor prototype for angle and pressure sensing with integration of transmission and sensing, which improve the integration of wearable devices. The experimental results show that the center frequency of the antenna varies with the bending angles, which can realize the angle sensing capacity according to the change of the antenna reflection coefficient. The graphene-based flexible microstrip patch antenna for angle sensing and information transmitting characteristics are tested. The results show that the reflection coefficient varies from -14.55dB to -23.8dB with the bending range from 0 degrees to 210 degrees. And the pressure sensing characteristics of the graphene film is also investigated. Experimental results prove that the flexible graphene antenna can combine angle sensing and signal transmission, which can be practically used as a flexible angle sensor. As for the graphene films-based pressure sensors, experimental results show that they are capable of detecting small pressures from 300Pa to 800Pa.
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