Browse Topic: Sensors and actuators
The rapid evolution of electric vehicles (EVs) necessitates advanced electronic control units (ECUs) for enhanced safety, monitoring, and performance. This study introduces an innovative ECU system designed with a modular architecture, incorporating real-time monitoring, cloud connectivity, and crash sensing. The methodology includes cost-effective design strategies, integrating STM32 controllers, CAN bus systems, and widely available sensors for motor RPM and temperature monitoring. Key findings demonstrate that the proposed ECU system improves data reliability, enhances vehicle safety through crash response systems, and enables predictive maintenance via cloud connectivity. This scalable and affordable ECU is adaptable to a broad range of EV models.
The U-Shift IV represents the latest evolution in modular urban mobility solutions, offering significant advancements over its predecessors. This innovative vehicle concept introduces a distinct separation between the drive module, known as the driveboard, and the transport capsules. The driveboard contains all the necessary components for autonomous driving, allowing it to operate independently. This separation not only enables versatile applications - such as easily swapping capsules for passenger or goods transportation - but also significantly improves the utilization of the driveboard. By allowing a single driveboard to be paired with different capsules, operational efficiency is maximized, enabling continuous deployment of driveboards while the individual capsules are in use. The primary focus of U-Shift IV was to obtain a permit for operating at the Federal Garden Show 2023. To achieve this goal, we built the vehicle around the specific requirements for semi-public road
We present DISRUPT, a research project to develop a cooperative traffic perception and prediction system based on networked infrastructure and vehicle sensors. Decentralized tracking and prediction algorithms are used to estimate the dynamic state of road users and predict their state in the near future. Compared to centralized approaches, which currently dominate traffic perception, decentralized algorithms offer advantages such as greater flexibility, robustness and scalability. Mobile sensor boxes are used as infrastructure sensors and the locally calculated state estimates are communicated in such a way that they can augment local estimates from other sensor boxes and/or vehicles. In addition, the information is transferred to a cloud that collects the local estimates and provides traffic visualization functionalities. The prediction module then calculates the future dynamic state based on neurocognitive behavior models and a measure of a road user's risk of being involved in
This SAE Recommended Practice defines the minimum performance specifications for sensors used within anthropomorphic test devices (ATDs) when performing impact tests per SAE J211. It is intended that any agency proposing to conduct tests in accordance with SAE J211 shall be able to demonstrate that the transducers they use would meet the performance requirements specified in this document.
This study introduces an innovative intelligent tire system capable of estimating the risk of total hydroplaning based on water pressure measurements within the tread grooves. Dynamic hydroplaning represents an important safety concern influenced by water depth, tread design, and vehicle longitudinal speed. Existing intelligent tire systems primarily assess hydroplaning risk using the water wedge effect, which occurs predominantly in deep water conditions. However, in shallow water, which is far more prevalent in real-world scenarios, the water wedge effect is absent at higher longitudinal speeds, which could make existing systems unable to reliably assess the total hydroplaning risk. Groove flow represents a key factor in hydroplaning dynamics, and it is governed by two mechanisms: water interception rate and water wedge pressure. In both the shallow water and deep water cases, the groove water flow will increase as a result of increasing the longitudinal speed of the vehicle for a
With many stakeholders involved, and major investments supporting it, the advancements in automated driving (AD) are undoubtedly there. Generally speaking, the motivation for advancing AD is driver convenience and road safety. Regarding the development of AD, original equipment manufacturers, technology start-ups, and AD systems developers have taken different approaches for automated vehicles (AVs). Some manufacturers are on the path toward stand-alone vehicles, mostly relying on onboard sensors and intelligence. On the other hand, the connected, cooperative, and automated mobility (CCAM) approach relies on additional communication and information exchange to ensure safe and secure operation. CCAM holds great potential to improve traffic management, road safety, equity, and convenience. In both approaches, there are increasingly large amounts of data generated and used for AD functions in perception, situational awareness, path prediction, and decision-making. The use of artificial
A major challenge in self-powered wearable sensors for health care monitoring is distinguishing different signals when they occur at the same time. Researchers from Penn State and China’s Hebei University of Technology addressed this issue by uncovering a new property of a sensor material, enabling the team to develop a new type of flexible sensor that can accurately measure both temperature and physical strain simultaneously but separately to more precisely pinpoint various signals.
A Northwestern University-led team of researchers has developed a new fuel cell that harvests energy from microbes living in dirt. About the size of a standard paperback book, the completely soil-powered technology could fuel underground sensors used in precision agriculture and green infrastructure. This potentially could offer a sustainable, renewable alternative to batteries, which hold toxic, flammable chemicals that leach into the ground, are fraught with conflict-filled supply chains and contribute to the ever-growing problem of electronic waste.
MEMS is a more complex technology than traditional semiconductors. They are 3D structures with moving parts, making them much more difficult to fabricate. If you’re designing a semiconductor, you may be able to take advantage of an existing process development kit (PDK), which your foundry can provide to you. There is no equivalent approach in MEMS. It’s a “one process, one product” paradigm that requires a high level of customization. That takes time, money, and resources.
In late July to October 2022, residents of the Manu’a Islands in American Samoa felt the earth shake several times a day, raising concerns of an imminent volcanic eruption or tsunami.
An invention that uses microchip technology in implantable devices and other wearable products such as smart watches can be used to improve biomedical devices including those used to monitor people with glaucoma and heart disease.
This document defines a set of standard application layer interfaces called JAUS Manipulator Services. JAUS Services provide the means for software entities in an unmanned system or system of unmanned systems to communicate and coordinate their activities. The Manipulator Services represent platform-independent capabilities commonly found across domains and types of unmanned systems. At present, twenty-five (25) services are defined in this document. These services are categorized as: Low Level Manipulator Control Services – The one service in this category allows for low-level command of the manipulator joint actuation efforts. This is an open-loop command that could be used in a simple tele-operation scenario. The service in this category is listed as follows: Primitive Manipulator Service Manipulator Sensor Services – These services, when queried, return instantaneous sensor data. Three services are defined that return respectively joint positions, joint velocities, and joint
Most 3D object detection methods employ LiDAR sensors to create 3D point clouds of their environment. Simply put, LiDAR sensors use laser beams to rapidly scan and measure the distances of objects and surfaces around the source. However, using LiDAR data alone can lead to errors due to the high sensitivity of LiDAR to noise, especially in adverse weather conditions like during rainfall.
Naval Air Systems Command Patuxent, MD navairpao@us.navy.mil
Researchers from Skoltech and the University of Texas at Austin have presented a proof-of-concept for a wearable sensor that can track healing in sores, ulcers, and other kinds of chronic skin wounds, even without the need to remove the bandages. The paper was published in the journal ACS Sensors.
Researchers have combined miniaturized hardware and intelligent algorithms to create a cost-effective, compact powerful tool capable of solving real-world problems in areas like healthcare.
Optical sensors serve as the backbone of numerous scientific and technological endeavors, from detecting gravitational waves to imaging biological tissues for medical diagnostics. These sensors use light to detect changes in properties of the environment they’re monitoring, including chemical biomarkers and physical properties like temperature. A persistent challenge in optical sensing has been enhancing sensitivity to detect faint signals amid noise.
Just one year after signing a ground-breaking trilateral agreement, the Deep Space Advanced Radar Capability partnership is completing facilities construction at the first of three sites that will host a global network of advanced ground-based sensors.
Advances in artificial intelligence (AI), machine learning (ML), and sensor fusion drive robotics functionality across many applications, including healthcare. Ongoing innovations in high-speed connectivity, edge computing, network redundancy, and fail-safe procedures crucial to optimizing robotics opportunities. The emergence of natural language processing and emotional AI functionality are poised to propel more intuitive, responsive, and adaptive human-machine interaction.
Defense Advanced Projects Research Agency (DARPA) Arlington, VA outreach@darpa.mil
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