Browse Topic: Military vehicles and equipment
Air Traffic Management (ATM) must be familiar with the exact Aircraft Take-off Weights (ATOWs) of airplanes to make the most use of runways, maintain safety margins high, and keep utilization and resources in balance. This paper aims to present a dependable ATOW forecasting methodology that can assist the air transport industry in enhancing operational decision-making. This research used datasets acquired from the EUROCONTROL Performance Review Commission (PRC) 2024 Aircraft Take-Off Weight Estimation dataset featuring 527,000 flights over Europe containing aircraft details, air trips and flight conditions. Technique comprises structured data input, inspection of missing data, timestamp aggregation to identify demand cycles over time, and domain-specific feature engineering using distance_per_minute, block_minutes, taxiout_ratio, and a strong wake turbulence metric The two supervised learning models used were Linear Regression (LR) for understanding and XGBoost for performance
Army researchers recently developed a 3D-printable, easy-to-assemble drone designed to enhance intelligence, surveillance and reconnaissance capabilities. Army Research Laboratory, Adelphi, MD Researchers at the U.S. Army Combat Capabilities Development Command, or DEVCOM, Army Research Laboratory (ARL) harnessed bottom-up Soldier innovation to develop an experimental 3D-printed small unmanned aerial system, or drone, that was demonstrated at the inaugural U.S. Army Best Drone Warfighter Competition in Huntsville, Alabama. Known as the Soldier Portable Autonomous Reconnaissance Transitioning Aircraft, or SPARTA, the drone was developed at DEVCOM ARL in collaboration with Soldiers. By incorporating Soldier feedback early in the design process and leveraging ARL's world-class research facilities, researchers developed a 3D-printable, easy-to-assemble drone designed to enhance intelligence, surveillance and reconnaissance capabilities. ARL is actively working to partner the technology
On a clear afternoon over a contested airspace, a drone suddenly appears on radar. Within seconds, more follow, and they're small, fast, and unpredictable. For the U.S. Army's air and missile defense operators, every moment counts. The difference between mission success and mission failure is measured in milliseconds. During that brief window, sensors must connect instantly, embedded systems must process floods of data at the edge, and command links must hold steady even under electronic interference.
The convergence of highly capable edge AI models and advanced commercial-off-the-shelf (COTS) edge AI accelerators is reshaping how computation is deployed across defense, aerospace, and commercial platforms. Mission-critical decisions increasingly must be made at the edge, onboard vehicles, satellites, and infrastructure nodes, where latency, connectivity, and power availability are constrained.
Leonardo DRS has opened a new naval power and propulsion manufacturing and testing facility in Charleston, South Carolina, expanding its role in delivering next generation electric propulsion, integrated power systems, and high energy payload support for U.S. Navy surface and undersea platforms. The 140,000 square foot site consolidates advanced manufacturing, final assembly, and high fidelity testing for electric power conversion and propulsion systems, while also supporting naval steam turbine design, production, and subsystem integration for programs including the Columbia class ballistic missile submarine. A representative for Leonardo's Naval Power Systems business unit provided emailed statements with details about the type of advanced manufacturing the company will deploy at the new facility.
The U.S. Army has selected two companies to develop prototype chassis and plug-in-cards for aviation and ground vehicles. The selection is the Army's latest milestone accomplishment in an effort to feature Modular Open System Approach (MOSA) -aligned embedded computing systems across all of the new investments it makes in technology upgrades for new and legacy vehicles. In a program update posted in September 2025, the Army selected General Dynamics Mission Systems and Pacific Defense as the lead developers for new C5ISR/EW Modular Open Suite of Standards (CMOSS) Mounted Form Factor prototypes. CMOSS is a set of standards developed by the U.S. Army to guide the design of embedded computing networks featured on Army vehicles.
During the 2025 Association of the United States Army (AUSA) annual meeting and exhibition, Forterra announced several major defense industry vehicle partnerships and introduced four new integrated modules designed to enable autonomy for military vehicles, communications, and more. Headquartered in Clarksburg, Maryland, Forterra develops autonomous mission systems for specific defense applications, including robotics and self-driving vehicles. The company has a new partnership with BAE Systems that will rapidly prototype an autonomous Armored Multi-Purpose Vehicle (AMPV). Separately, Forterra has also collaborated with Oshkosh Defense and Raytheon to develop the “DeepFires” autonomous vehicle launcher technology.
RF and fiber have long co-existed within modern military and aerospace systems, with each medium dedicated to separate, mission-critical roles. Increasingly, however, system designers are turning to RF-over-fiber (RFoF) architectures to bridge the gap between over-the-air RF interfaces and the long, interference-resistant transport advantages of fiber. When it comes to over-the-air communications uses like tactical radio or satellite communications terminals, radio frequency (RF) is still the dominant signal format. RF is also commonly used at the front end of radar and electronic warfare, supporting search, tracking, fire control radar, missile seekers, jammers and electronic support measures.
Pyrovalves (also known as pyrotechnic valves) have long been a staple in defense systems, particularly in missile and munition launcher applications. The rapid growth of counter-UAS and missile defense systems makes this an ideal time to explore smarter alternatives to pyrovalves. One of the largest ongoing U.S. military efforts is the Missile Defense Agency's (MDA) Scalable Homeland Innovative Enterprise Layered Defense (SHIELD) Multiple Award Indefinite Delivery/Indefinite Quantity (IDIQ) contract. In December, MDA issued two tranches of SHIELD awards to more than 2,100 companies, including major defense contractors and startups such as Lockheed Martin, Raytheon, Boeing, Shield AI, Anduril, and Virtualitics.
The U.S. Army Space and Missile Defense Command Technical Center's Aerophysics Research Facility, (ARF), fired a successful hypersonic shot to test its new rainfield simulator. U.S. Army Space and Missile Defense Command Technical Center, Huntsville, AL Zack Perrin, ARF manager and technical lead engineer of the U.S. Army Space and Missile Defense Command (USASMDC's) Targets and Test Resources Branch of the Ronald Reagan Ballistic Missile Defense Test Site, said ARF is SMDC's premier hypersonic flight and hypervelocity impact laboratory. Perrin said their largest gun system, the 254 mm light gas guns, or LGGs, is the fastest gun in the Army and can launch projectiles 6 inches in diameter to speeds up to 3 kilometers per second or smaller projectiles on the order of 2.7 inches in diameter to velocities exceeding 6 km/s. “I like to tell people that the facility is a gun range the size of an aircraft carrier and within the facility are multiple engineering tools, called light gas guns
Current world conflicts have proven that drones are now indispensable tools in modern warfare. Whether for reconnaissance, loitering munitions, or asymmetric tactics that exploit vulnerabilities in conventional defenses, unmanned aerial systems (UAS) are redefining the rules of engagement.
Since the emergence of the first tanks in World War I, tracked military vehicles have driven the development of increasingly sophisticated control systems, keeping pace with the evolution of technologies and combat tactics. This study aims to develop a longitudinal speed control system for tracked military vehicles using a cascade framework. To this end, a dynamic model based on the bicycle model—commonly employed for wheeled vehicles—has been appropriately adapted to represent the dynamics of tracked vehicles. In the first stage, a Model-based Predictive Controller defines the required traction force to be produced by the track; subsequently, a PID controller determines the necessary torque on the drive pulley to achieve the desired force. Simulations performed in MATLAB, considering a straight trajectory and speeds of up to 20 km/h, demonstrate the effectiveness of the proposed control system, yielding satisfactory results in the regulation of longitudinal speed.
Augustine's Law predicts “In the year 2054, the entire defense budget of the United States will purchase just one aircraft. This aircraft will have to be shared by the Air Force and Navy three days each per week except for leap year, when it will be made available to the Marines for the extra day.” While the world is not on course for the $800 billion aircraft as Augustine predicted, the aerospace & defense industry must take steps to bring new technology to the battlefield without the $800 billion price tag. The development of robotic aircraft or drones is one way to deliver new capability faster for less cost.
Modern warfare is defined as much by data dominance as by maneuver. From satellite-based intelligence, surveillance, and reconnaissance (ISR) platforms to dismounted soldiers' handheld radios, operational success depends on the ability to move, process, and act on digital information in real time. Yet this dependence introduces a critical vulnerability: as the force becomes more data-centric, it becomes more susceptible to disconnection, jamming, and cyber denial. In disconnected, intermittent, and limited (DIL) environments - where communications are degraded by terrain, adversarial interference, or limited infrastructure - traditional network architectures falter. Centralized command nodes and linear data pipelines cannot sustain the agility or resilience required at the tactical edge. The solution is a new design paradigm - one that integrates ruggedized hardware, edge computing, artificial intelligence (AI), and hybrid tactical-cloud architectures into a distributed, adaptive
Leonardo DRS Arlington, VA mmount@drs.com
Moog Inc. East Aurora, NY kgibas@moog.com
This SAE Standard applies to all combinations of pneumatic tires, wheels, or runflat devices (only as defined in SAE J2013) for military tactical wheeled vehicles only as defined in SAE J2013. This applies to original equipment and new replacement tires, retread tires, wheels, or runflat devices. This document describes tests and test methodology, which will be used to evaluate and measure tire/wheel/runflat system and changes in vehicle performance. All of the tests included in this document are not required for each tire/wheel/runflat assembly. The Government Tire Engineering Office and Program Office for the vehicle system have the responsibility for the selection of a specific test(s) to be used. The selected test(s) should be limited to that required to evaluate the tire/wheel/runflat system and changes in vehicle performance. Selected requirements of this specification shall be used as the basis for procurement of a tire, wheel, and/or runflat device for military tactical wheeled
U.S. Army soldiers recently evaluated the off-road delivery capabilities of Overland AI's “ULTRA” autonomous vehicle during a demonstration exercise in Vaziani, Georgia. U.S. Army, Vaziani, Georgia In an effort to cut costs and improve supply delivery efficiency, the U.S. Army assessed the Overland AI ULTRA Fully Autonomous Tactical Vehicle prototype during exercise Agile Spirit 25 at the Combat Training Center, Vaziani Training Area, Georgia, in July. “Agile Spirit 25 is the 12th iteration of a biennial multinational exercise designed to enhance readiness, interoperability and combined operational capabilities, which promotes our countries' shared goal of security and stability in the Black Sea Region,” said Col. Will Cox, Co-exercise Director for Agile Spirit 25.
Hensoldt Taufkirchen, Germany nico.fritz@hensoldt.net
In an effort to cut costs and improve supply delivery efficiency, the U.S. Army assessed the Overland AI ULTRA Fully Autonomous Tactical Vehicle prototype during exercise Agile Spirit 25 at the Combat Training Center, Vaziani Training Area, Georgia, in July.
When a Marine in the field launches an uncrewed aerial vehicle (UAV) to gather intelligence, it becomes more than just a drone. It's a flying data center that processes AI workloads, runs machine learning algorithms, and transmits critical information through a complex network designed to provide situational awareness across multiple commands. All of this computational power generates significant heat, and in the confined space of a UAV operating in harsh environmental conditions, thermal management becomes critical to mission success. But there's a fundamental question the U.S. defense isn't asking: how will we manage the heat? The Golden Dome, the Trump administration's vision for missile defense, builds upon the existing Joint All-Domain Command and Control (JADC2) framework for connecting sensors from all branches of the U.S. armed forces into a unified network powered by artificial intelligence. This plan faces an existential threat from thermal management challenges that have
Forest fire prevention and control agencies in São Carlos, in the interior of the state of São Paulo, Brazil, will soon have help from the sky to detect fires more quickly and combat them before they grow out of control and cannot be extinguished.
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
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