Browse Topic: Automation
Measuring the volume of harvested material behind the machine can be beneficial for various agricultural operations, such as baling, dropping, material decomposition, cultivation, and seeding. This paper aims to investigate and determine the volume of material for use in various agricultural operations. This proposed methodology can help to predict the amount of residue available in the field, assess field readiness for the next production cycle, measure residue distribution, determine hay readiness for baling, and evaluate the quantity of hay present in the field, among other applications which would benefit the customer. Efficient post-harvest residue management is essential for sustainable agriculture. This paper presents an Automated Offboard System that leverages Remote Sensing, IoT, Image Processing, and Machine Learning/Deep Learning (ML/DL) to measure the volume of harvested material in real-time. The system integrates onboard cameras and satellite imagery to analyze the field
Over the past 25 years, the heavy fabrication and construction equipment industry has experienced significant transformation. Driven by a global surge in demand for construction machinery, manufacturers are under increasing pressure to deliver higher volumes within shorter timelines and at competitive costs. This demand surge has been compounded by workforce-related challenges, including a declining interest among the new generation in acquiring traditional manufacturing skills such as welding, heat treatment, and painting. Furthermore, the industry faces difficulties in staffing third-shift operations, which are essential to meet production targets. The adoption of automation technologies in heavy fabrication and construction equipment manufacturing has been gradual and often hindered by legacy product designs that were optimized for conventional manufacturing methods. As the industry transitions toward smart, connected manufacturing environments under the industry 4.0 paradigm, it
Keshika Warnakula is a Senior Flight Mechanics Engineer at Syos Aerospace Limited and the winner of the 2025 Rising Stars Award Aerospace and Defense category. Syos Aerospace is based in Mount Maunganui, New Zealand, specializing in robotics engineering and the development of autonomous air, land, and sea vehicles. The company also has an office located in Fareham, UK, and was recently named New Zealand's “Hi-Tech Company Of the Year.”
This specification covers a premium aircraft-quality, low-alloy steel in the form of bars, forgings, mechanical tubing, and forging stock.
It is expected that Level 4 and 5 automated driving systems-dedicated vehicles (ADS-DVs) will eventually enable persons to travel at will who are otherwise unable to obtain a driver’s license for a conventional vehicle, namely, persons with certain visual, cognitive, and/or physical impairments. This information report focuses on these disabilities but also provides guidance for those with other disabilities. This report is limited to fleet-operated, on-demand, shared mobility scenarios, as this is widely considered to be the first way people will be able to interact with ADS-DVs. To be more specific, this report does not address fixed-route transit services or private vehicle ownership. Similarly, this report is focused on motor vehicles (refer to SAE J3016), not scooters, golf carts, etc. Lastly, this report does not address the design of chair lifts, ramps, or securements for persons who use wheeled mobility devices (WHMD) (e.g., wheelchair, electric cart, etc.), as these matters
As unmanned vehicular networks become more prevalent in civilian and defense applications, the need for robust security solutions grows in parallel. While ROS 2 offers a flexible platform for robotic operations, its security model lacks the adaptability required for dynamic trust management and proactive threat mitigation. To address these shortcomings, we propose a novel framework that integrates containerized ROS 2 nodes with Kubernetes-based orchestration, a dynamic trust management subsystem, and integrability with simulators for real-time and protocol-flexible network simulation. By embedding trust management directly within each ROS 2 container and leveraging Kubernetes, we overcome ROS 2’s security limitations by enabling real-time monitoring and machine learning-driven anomaly detection (via an autoencoder trained on custom data), facilitating the isolation or removal of suspicious nodes. Additionally, Kubernetes policies allow seamless scaling and enforcement of trust-based
Employment of Robotic and Autonomous Systems requires a different paradigm of mission planning, one which considers not only the tasks to be performed by the RAS themselves but regards the flow of information to support the observability of the RAS by the operator. GTRI has developed an initial capability for mission planning of mixed motive, heterogeneous, autonomous systems for management of macro level metrics that support the decision making of the operator or user during employment. The work is ongoing, extensible to additional capability sets, and modular to support integration of other autonomous capabilities.
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
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.
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.
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
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.
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.
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.
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.
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