Register now free-of-charge to explore this white paper How Embodied Intelligence Enhances the Safety, Resilience, and Autonomy of UAV Systems As drones evolve into critical agents across defense, disaster response, and infrastructure inspection, they must become more adaptive, secure, and resilient. Traditional AI methods fall short in real-world unpredictability. This whitepaper from the Technology Innovation Institute (TII) explores how Embodied AI - AI that integrates perception, action, memory, and learning in dynamic environments, can revolutionize drone operations. Drawing from innovations in GenAI, Physical AI, and zero-trust frameworks, TII outlines a future where drones can perceive threats, adapt to change, and collaborate safely in real time. The result: smarter, safer, and more secure autonomous aerial systems. What Attendees will Learn: Why Embodied AI Outperforms Traditional AI The 4 Pillars of Drone Intelligence Swarm Resilience in Dynamic Environments Security Breakthroughs for Critical Missions Click on the cover to download the white paper PDF now.

An Israeli drone strike that has killed one person in a south Lebanon village targeted a municipal worker operating a water well, not a Hezbollah member as the Israeli military had claimed, according to the Mayor of Nabatieh al-Fawqa Zein Ali Ghandour. Ghandour said on Thursday that the victim, Mahmoud Hasan Atwi, was "martyred" while on his official duty of trying to provide water for the people of the town. "We condemn in the strongest terms this blatant aggression against civilians and civilian infrastructure as well as the Lebanese state and its institutions," the mayor said in a statement. Ghandour called on the international community to press the issue and put an end to Israeli violations. The Israeli military had claimed that it fired at a "Hezbollah operative" who it said was "rehabilitating a site" used by the group.

Imagine: China deploys hundreds of thousands of autonomous drones in the air, on the sea, and under the water--all armed with explosive warheads or small missiles. These machines descend in a swarm toward military installations on Taiwan and nearby US bases, and over the course of a few hours, a single robotic blitzkrieg overwhelms the US Pacific force before it can even begin to fight back. The proliferation of cheap drones means just about any group with the wherewithal to assemble and launch a swarm could wreak havoc, no expensive jets or massive missile installations required. The US armed forces are now hunting for a solution--and they want it fast. Every branch of the service and a host of defense tech startups are testing out new weapons that promise to disable drones en masse.

While the US has precision missiles that can shoot these drones down, they don't always succeed: A drone attack killed three US soldiers and injured dozens more at a base in the Jordanian desert last year. And each American missile costs orders of magnitude more than its targets, which limits their supply; countering thousand-dollar drones with missiles that cost hundreds of thousands, or even millions, of dollars per shot can only work for so long, even with a defense budget that could reach a trillion dollars next year. The US armed forces are now hunting for a solution--and they want it fast. Every branch of the service and a host of defense tech startups are testing out new weapons that promise to disable drones en masse. There are drones that slam into other drones like battering rams; drones that shoot out nets to ensnare quadcopter propellers; precision-guided Gatling guns that simply shoot drones out of the sky; electronic approaches, like GPS jammers and direct hacking tools; and lasers that melt holes clear through a target's side.

Unmanned Aircraft Systems (UAS) and satellites are key data sources for precision agriculture, yet each presents trade-offs. Satellite data offer broad spatial, temporal, and spectral coverage but lack the resolution needed for many precision farming applications, while UAS provide high spatial detail but are limited by coverage and cost, especially for hyperspectral data. This study presents a novel framework that fuses satellite and UAS imagery using super-resolution methods. By integrating data across spatial, spectral, and temporal domains, we leverage the strengths of both platforms cost-effectively. We use estimation of cover crop biomass and nitrogen (N) as a case study to evaluate our approach. By spectrally extending UAS RGB data to the vegetation red edge and near-infrared regions, we generate high-resolution Sentinel-2 imagery and improve biomass and N estimation accuracy by 18% and 31%, respectively. Our results show that UAS data need only be collected from a subset of fields and time points. Farmers can then 1) enhance the spectral detail of UAS RGB imagery; 2) increase the spatial resolution by using satellite data; and 3) extend these enhancements spatially and across the growing season at the frequency of the satellite flights. Our SRCNN-based spectral extension model shows considerable promise for model transferability over other cropping systems in the Upper and Lower Chesapeake Bay regions. Additionally, it remains effective even when cloud-free satellite data are unavailable, relying solely on the UAS RGB input. The spatial extension model produces better biomass and N predictions than models built on raw UAS RGB images. Once trained with targeted UAS RGB data, the spatial extension model allows farmers to stop repeated UAS flights. While we introduce super-resolution advances, the core contribution is a lightweight and scalable system for affordable on-farm use.

The flight time of multirotor unmanned aerial vehicles (UAVs) is typically constrained by their high power consumption. Tethered power systems present a viable solution to extend flight times while maintaining the advantages of multirotor UAVs, such as hover capability and agility. This paper addresses the critical aspect of cable selection for tether-powered multirotor UAVs, considering both hover and forward flight. Existing research often overlooks the trade-offs between cable mass, power losses, and system constraints. We propose a novel methodology to optimize cable selection, accounting for thrust requirements and power efficiency across various flight conditions. The approach combines physics-informed modeling with system identification to combine hover and forward flight dynamics, incorporating factors such as motor efficiency, tether resistance, and aerodynamic drag. This work provides an intuitive and practical framework for optimizing tethered UAV designs, ensuring efficient power transmission and flight performance. Thus allowing for better, safer, and more efficient tethered drones.

Ukrainian President Volodymyr Zelenskyy is set to meet with German Chancellor Friedrich Merz, as Ukraine seeks further military support amid a recent escalation in Russia's bombing campaign, despite United States-led efforts to end the war. During their talks in Berlin on Wednesday, Zelenskyy and Merz are also expected to discuss sanctions on Russia. According to a German government spokesperson, Merz will receive Zelenskyy with military honours at the federal chancellery at 10:00 GMT. The Berlin talks follow Russia and Ukraine's direct face-to-face talks in Turkiye earlier in May. Despite pressure from United States President Donald Trump to end the war, the talks failed to produce a ceasefire agreement.

Lockheed Martin CEO Jim Taiclet weighs in on the Trump administration's Golden Dome defense system announcement on'Special Report.' Lockheed Martin CEO Jim Taiclet said President Donald Trump's proposed "Golden Dome" missile shield for the United States is a "fantastic vision" for the country as defense contracting companies work to implement the commander-in-chief's bold idea by the end of his term. "We'll be able to use the Golden Dome concept to make sure the country is increasingly protected against hypersonic threats," Taiclet said in an exclusive interview Tuesday on "Special Report." Trump unveiled his ambitious missile defense plan at the White House last week, which he says will be operational by the time he leaves office. The announcement comes as the United States faces growing threats from adversaries around the world who are making significant inroads in artificial intelligence and drone technology.

The safety of autonomous systems in dynamic and hazardous environments poses significant challenges. This paper presents a testing approach named SCALOFT for systematically assessing the safety of an autonomous aerial drone in a mine. SCALOFT provides a framework for developing diverse test cases, real-time monitoring of system behaviour, and detection of safety violations. Detected violations are then logged with unique identifiers for detailed analysis and future improvement. SCALOFT helps build a safety argument by monitoring situation coverage and calculating a final coverage measure. We have evaluated the performance of this approach by deliberately introducing seeded faults into the system and assessing whether SCALOFT is able to detect those faults. For a small set of plausible faults, we show that SCALOFT is successful in this.

Path planning is a critical component in autonomous drone operations, enabling safe and efficient navigation through complex environments. Recent advances in foundation models, particularly large language models (LLMs) and vision-language models (VLMs), have opened new opportunities for enhanced perception and intelligent decision-making in robotics. However, their practical applicability and effectiveness in global path planning remain relatively unexplored. This paper proposes foundation model-guided path planners (FM-Planner) and presents a comprehensive benchmarking study and practical validation for drone path planning. Specifically, we first systematically evaluate eight representative LLM and VLM approaches using standardized simulation scenarios. To enable effective real-time navigation, we then design an integrated LLM-Vision planner that combines semantic reasoning with visual perception. Furthermore, we deploy and validate the proposed path planner through real-world experiments under multiple configurations. Our findings provide valuable insights into the strengths, limitations, and feasibility of deploying foundation models in real-world drone applications and providing practical implementations in autonomous flight. Project site: https://github.com/NTU-ICG/FM-Planner.