Browse Topic: Automation

Items (3,120)
Bearings are essential mechanical components that support external loads and facilitate rotational motion. With the increasing demand for high-performance applications in industries such as semiconductors, aerospace, and robotics, the need for accurate and robust performance evaluation has intensified. Traditionally, bearing performance has been assessed using static or quasi-static theoretical approaches. However, these methods are limited in their ability to capture time-dependent behaviors, which are critical in real-world applications. In this study, a rigid body dynamics analysis was proposed to evaluate the time-dependent behavior of bearings. The methodology was first applied to a deep groove ball bearing, and the results were compared with those obtained from bearing theory to validate the approach. Subsequently, the method was extended to an automotive wheel bearing, and the time-dependent contact angles and ball loads were analyzed under axial and radial loading conditions
Lee, Seungpyo
The growing emphasis on road safety and environmental sustainability has spurred the development of technologies to enhance vehicle efficiency. Accurate vehicle mass knowledge is crucial for all vehicles, to optimize advanced driver assistance systems (ADAS) and CCAM (Connected, Cooperative, and Automated Mobility) systems, as well as to improve both safety and energy consumption. Moreover, the continuous need to report precisely on the greenhouse emissions for good transports is becoming a key point to certificate the impact of transportation systems on the environment. Mass influences longitudinal dynamics, affecting parameters such as rolling resistance and inertia, which in turn are critical to adaptive control strategies. Moreover, the knowledge of vehicle mass represents a key challenge and a fundamental aspect for fleet managers of heavy-duty vehicles. Typically, this information is not readily available unless obtained through high-cost weighing systems or estimated
Vicinanza, MatteoAdinolfi, Ennio AndreaPianese, Cesare
Mobileye announced in June that its ongoing work with Volkswagen will deliver the automaker's first production SAE Level 4 autonomous vehicles sometime in 2026. The first of these vehicles will be the Volkswagen ID. Buzz AV, which will use the Mobileye Drive autonomous platform and will most likely deploy first in the U.S next year. The ID. Buzz AV is one of four programs Mobileye is working on with VW, Dan Galves, chief communications officer at Mobileye, told SAE Media, and the variety and size of the programs will be key to making AVs scale. The vehicles in each of these programs use the same Mobileye core, with similar cameras and sensors and the same system on chip (SOC), even as the details differ.
Blanco, Sebastian
EPFL researchers have developed a customizable soft robotic system that uses compressed air to produce shape changes, vibrations, and other haptic, or tactile, feedback in a variety of configurations. The device holds significant promise for applications in virtual reality, physical therapy, and rehabilitation.
This SAE Recommended Practice provides DA metrics used to quantify the DDT performance of ADS-operated vehicles.3 Here, the primary focus is on the safety-related DDT performance and includes definitions, taxonomy, characteristics, and usage (along with alternatives) for each metric. DDT performance is a subset of overall operational performance of ADS-operated vehicles. Thus, assessments of DDT Fallback [1], cybersecurity, maintenance, interactions with passengers, etc., while important and could have an indirect impact on the DDT, are out of scope for this document. Note that the DA metrics do not specify the actions and/or maneuvers to be executed by the (ADS-operated) subject vehicle (SV). While this document presents a set of individual DA metrics, it is important to note that it is out of the scope of this document to describe how these metrics should be applied in practice. This is because the overall context of the scenario or deployment must be considered during DA metrics
On-Road Automated Driving (ORAD) Committee
Advanced technologies that assist the human driver or reduce (or even eliminate) the human driver’s role are becoming increasingly prevalent in new light-duty vehicles used by the general public. These technologies are divided between Active Safety features that monitor the human driver and vehicle motion and act intermittently to mitigate and avoid crashes, and Driving Automation features that assume some or all of the dynamic driving task from the human driver. Both types of technologies have the potential to reduce injuries and save lives by reducing the frequency and/or severity of crashes. Safety Impacts of Active Safety and Driving Automation Features addresses the current capabilities and future potential for Active Safety and Driving Automation features to reduce crash frequency and severity and provides an overview of the state of the industry for both types of features, including current deployments, trends, and anticipated rollouts. Gaps in knowledge, unsettled issues, and
Wishart, Jeffrey
The automation of labor-intensive picking and planting operations is having an immediate impact in the agricultural indutry. In its simplest form, robotic automation can reduce the labor and soil disturbance while enabling organic soil cover and increasing species diversification through precision approaches to planting, weeding, and spraying. With this, pesticides and fertilizers can be applied in a more targeted way, and with machinery visiting fields more frequently, earlier and more targeted intervention can occur before pests become established. Small, Mobile, and Autonomous Agricultural Robots identifies issues that need to be resolved fo for this technology to thrive, including improving methods of acquiring and labeling training data to facilitate more accurate models for specific applications. It also discusses concepts such as general-purpose mechanical platforms for use as carriers of agricultural automation systems with high stability, positional accuracy, and variable
Muelaner, Jody E.
Perception is a key component of automated vehicles (AVs). However, sensors mounted to the AVs often encounter blind spots due to obstructions from other vehicles, infrastructure, or objects in the surrounding area. While recent advancements in planning and control algorithms help AVs react to sudden object appearances from blind spots at low speeds and less complex scenarios, challenges remain at high speeds and complex intersections. Vehicle-to-infrastructure (V2I) technology promises to enhance scene representation for connected and automated vehicles (CAVs) in complex intersections, providing sufficient time and distance to react to adversary vehicles violating traffic rules. Most existing methods for infrastructure-based vehicle detection and tracking rely on LIDAR, RADAR, or sensor fusion methods, such as LIDAR–camera and RADAR–camera. Although LIDAR and RADAR provide accurate spatial information, the sparsity of point cloud data limits their ability to capture detailed object
Saravanan, Nithish KumarJammula, Varun ChandraYang, YezhouWishart, JeffreyZhao, Junfeng
Specialized robots that can both fly and drive typically touch down on land before attempting to transform and drive away. But when the landing terrain is rough, these robots sometimes get stuck and are unable to continue operating. Now a team of Caltech engineers has developed a real-life Transformer that has the “brains” to morph in midair, allowing the dronelike robot to smoothly roll away and begin its ground operations without pause. The increased agility and robustness of such robots could be particularly useful for commercial delivery systems and robotic explorers.
A human clearing junk out of an attic can often guess the contents of a box simply by picking it up and giving it a shake, without the need to see what’s inside. Researchers from MIT, Amazon Robotics, and the University of British Columbia have taught robots to do something similar.
San Francisco startup Canvas has developed a robotic system handling one of the most labor-intensive trades in construction: drywall finishing. Leveraging robotic arms from Universal Robots, Canvas has built a machine that reduces the usual five to seven days of spraying and sanding the drywall to just around two days for both Level 4 and Level 5 finishes.
Ready for that long-awaited summer vacation? First, you’ll need to pack all the items required for your trip into a suitcase, making sure everything fits securely without crushing anything fragile. Because humans possess strong visual and geometric reasoning skills, this is usually a straightforward problem, even if it may take a bit of finagling to squeeze everything in.
Warehouse logistics increasingly rely on automation in the form of autonomous mobile robots (AMRs), scanners, complex conveyors, and fleet management systems for seamless operation, but it’s the ubiquitous, century-old pallet that remains the critical support system. Make no mistake, if even one of those thousands of pallets is defective, it can create havoc in the warehouse.
Researchers have created a light-powered soft robot that can carry loads through the air along established tracks, similar to cable cars or aerial trams. The soft robot operates autonomously, can climb slopes at angles of up to 80°, and can carry loads up to 12 times its weight.
Imagine a robot that can walk, without electronics, and only with the addition of a cartridge of compressed gas, right off the 3D printer. It can also be printed in one go, from one material.
The wealth of information provided by our senses that allows our brain to navigate the world around us is remarkable. Touch, smell, hearing, and a strong sense of balance are crucial to making it through what to us seem like easy environments such as a relaxing hike on a weekend morning.
Not a traditional university lab, Harvard University’s Move Lab employs professional engineers, product developers, and academics who work across disciplines to bring research innovations to market. The lab is focused on human performance enhancement to protect people’s physical ability to guard against injury, extend their abilities beyond the limits of advancing age, and restore them to people who have lost them. They have developed wearable solutions that support functional movements and allow impaired individuals to more easily interact with their environment.
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
Beutenmüller, FrankBrostek, LukasDoberstein, ChristianHan, LongfeiKefferpütz, KlausObstbaum, MartinPawlowski, AntoniaRössert, ChristianSas-Brunschier, LucasSchön, ThiloSichermann, Jörg
Computer-aided synthesis and development tools are essential for discovering and optimizing innovative concepts. Evaluating different concepts and making informed decisions relies heavily on accurate assessments of system properties. Estimating these properties in the early stages of vehicle development is challenging due to the depth of modelling required. In order to enable a cost prognosis for driving assistance and automated driving functions including software and hardware properties a cost model was developed at the Institute of Automotive Engineering. The methodology and cost model focuses on multiple combined approaches. This includes a bottom-up approach for the hardware. The costs of the software components are integrated into the model with the help of existing literature data and an exponential regression. For a comprehensive view of the total costs, the model is the model is also supplemented by a top-down approach for estimating the costs of other hardware components. The
Sturm, AxelHichri, BassemRohde García, ÁlvaroHenze, Roman
This paper deals with autonomous vehicle trajectory planning for avoidance maneuver. It introduces a trajectory planning approach that allows for static obstacle avoidance maneuvers. Specifically, this study proposes a generalized geometric formulation based on Sigmoid functions in order to generate a smooth path that guides the vehicle on a lateral deviation and returns to the initial lane. In addition, the method considers various geometrical and dynamic constraints to ensure vehicle stability while taking into account a safety area around the obstacle. The algorithm validation is conducted on the professional CarMaker simulator by associating the path generation module with a robust lateral tracking controller. The results demonstrate the effectiveness of the proposed planning method in the field of autonomous driving vehicle control.
Vigne, BenoitGiuliani, Pio MicheleOrjuela, RodolfoBasset, Michel
Human driver errors, such as distracted driving, inattention, and aggressive driving, are the leading causes of road accidents. Understanding the underlying factors that contribute to these behaviors is critical for improving road safety. Previous studies have shown that physiological states, like raised heart rates due to stress and anxiety, can influence driving behavior, leading to erratic driving and an increased risk of accidents. In this study, we conducted on-road tests using a measurement system based on the Driver-Driven vehicle-Driving environment (3D) method. We collected physiological signals, specially electrocardiography (ECG) data, from human drivers to examine the relationship between physiological states and driving behaviors. The aim was to determine whether ECG can serve as an indicator of potential risky driving behaviors, such as sudden acceleration and frequent steering adjustments. This information enables automated driving (AD) systems to intervene in dangerous
Ji, DejieFlormann, MaximilianBollmann, JulianHenze, RomanDeserno, Thomas M.
While semi-autonomous driving (SAE level 3 & 4) is already partially a reality, the driver still needs to take over driving upon notice. Hence, the cockpit cannot be designed freely to accommodate spaces for non-driving related activities. In the following use case, a mobile workplace is created by integrating a translucent acrylic glass pane into the cockpit and introducing joystick steering of the car. By using the technology Virtual Desktop 1, which is a software layer, any desktop application can be represented freely transformable on arbitrary physical and virtual surfaces. Thus, a complete Windows environment can be distributed across all curved and flat surfaces of an interior. The concept is further enhanced by a voice-driven generative AI which helps to summarize documents. A physical and a virtual demonstrator are created to experience and assess the mobile workspace, the well-being of the driver, external influences, and psychological aspects. The physical demonstrator is a
Beutenmüller, FrankReining, NineRosenstiel, RetoSchmidt, MaximilianLayer, SelinaBues, MatthiasMendonca, Daisy
Trajectory planning is a major challenge in robotics and autonomous vehicles, ensuring both efficient and safe navigation. The primary objective of this work is to generate an optimal trajectory connecting a starting point to a destination while meeting specific requirements, such as minimizing travel distance and adhering to the vehicle’s kinematic and dynamic constraints. The developed algorithms for trajectory design, defined as a sequence of arcs and straight segments, offer a significant advantage due to their low computational complexity, making them well-suited for real-time applications in autonomous navigation. The proposed trajectory model serves as a benchmark for comparing actual vehicle paths in trajectory control studies. Simulation results demonstrate the robustness of the proposed method across various scenarios.
Soundouss, HalimaMsaaf, MohammedBelmajdoub, Fouad
Engineers have designed robots that crawl, swim, fly, and even slither like a snake, but no robot can hold a candle to a squirrel, which can parkour through a thicket of branches, leap across perilous gaps and execute pinpoint landings on the flimsiest of branches.
For the team at SmartCap, building top-notch gear for outdoor adventurers isn’t just a business — it’s a passion driven by their own love for the wild. But as demand for their rugged, modular truck caps soared after their move to North America in 2022, they hit a snag: How do you ramp up production without sacrificing the meticulous quality you are known for, all while navigating a tough labor market? Their answer? A bold step into the world of intelligent automation, teaming up with GrayMatter Robotics, and employing the company’s innovative Scan&Sand™ system.
Researchers have developed a tiny magnetic robot that can take 3D scans from deep within the body and could revolutionize early cancer detection.
A team of UCLA engineers and their colleagues have developed a new design strategy and 3D printing technique to build robots in one single step. The breakthrough enabled the entire mechanical and electronic systems needed to operate a robot to be manufactured all at once by a new type of 3D printing process for engineered active materials with multiple functions (also known as metamaterials). Once 3D printed, a “meta-bot” will be capable of propulsion, movement, sensing, and decision-making.
The global medical device manufacturing industry is undergoing a rapid transformation driven by technological innovation, automation, and increasing demands for customized, high-quality care. For engineers at the heart of medtech manufacturing, understanding the latest technologies is crucial not only for maintaining competitiveness but also for ensuring regulatory compliance, improving time to market, and optimizing production workflows.
Swimming robots play a crucial role in mapping pollution, studying aquatic ecosystems, and monitoring water quality in sensitive areas such as coral reefs or lake shores. However, many devices rely on noisy propellers, which can disturb or harm wildlife. The natural clutter in these environments — including plants, animals, and debris — also poses a challenge to robotic swimmers.
It’s a game a lot of us played as children — and maybe even later in life: unspooling measuring tape to see how far it would extend before bending. But to engineers at the University of California San Diego, this game was an inspiration, suggesting that measuring tape could become a great material for a robotic gripper.
Repartly, a startup based in Guetersloh, Germany, is using ABB’s collaborative robots to repair and refurbish electronic circuit boards in household appliances. Three GoFa cobots handle the sorting, visual inspection and precise soldering tasks enabling the company to enhance efficiency and maintain high quality standards.
Low-cost jelly-like materials, developed by researchers at the University of Cambridge, can sense strain, temperature, and humidity. And unlike earlier self-healing robots, they can also partially repair themselves at room temperature.
Innovators at NASA Johnson Space Center have developed a robotic system whose primary structural platform, or “orb,” can be injected into a pipe network and perform reconnaissance of piping infrastructure and other interior volumes. When deployed, this technology uses throttled fluid flow from a companion device for passive propulsion. A tethered line facilitates directional control by the orb’s operator, allowing it to navigate through various piping configurations, including 90° junctions.
When we last heard from MELD Manufacturing, the large-scale 3D printer supplier was taking first place in the Robotics/Automation/Manufacturing category at the 2018 .
Letter from the Guest Editors
Liang, CiTörngren, Martin
Industries that require high-accuracy automation in the creation of high-mix/low-volume parts, such as aerospace, often face cost constraints with traditional robotics and machine tools due to the need for many pre-programmed tool paths, dedicated part fixtures, and rigid production flow. This paper presents a new machine learning (ML) based vision mapping and planning technique, created to enhance flexibility and efficiency in robotic operations, while reducing overall costs. The system is capable of mapping discrete process targets in the robot work envelope that the ML algorithms have been trained to identify, without requiring knowledge of the overall assembly. Using a 2D camera, images are taken from multiple robot positions across the work area and are used in the ML algorithm to detect, identify, and predict the 6D pose of each target. The algorithm uses the poses and target identifications to automatically develop a part program with efficient tool paths, including
Langan, DanielHall, MichaelGoldberg, EmilySchrandt, Sasha
Industrial bearings are critical components in aerospace, industrial, and automotive manufacturing, where their failures can result in costly downtime. Traditional fault diagnosis typically depends on time-consuming on-site inspections conducted by specialized field engineers. This study introduces an automated Artificial Intelligence virtual agent system that functions as a maintenance technician, empowering on-site personnel to perform preliminary diagnoses. By reducing the dependence on specialized engineers, this technology aims to minimize downtime. The Agentic Artificial Intelligence system leverages agents with the backbone of intelligence from Computer Vision and Large Language Models to guide the inspection process, answer queries from a comprehensive knowledge base, analyze defect images, and generate detailed reports with actionable recommendations. Multiple deep learning algorithms are provisioned as backend API tools to support the agentic workflow. This study details the
Chandrasekaran, Balaji
Items per page:
1 – 50 of 3120