A large-scale Russian drone attack in the southeastern Ukrainian city of Dnipro has killed three people, including a young girl, according to the regional governor, hours before officials from the United States, Europe and Ukraine gather in Paris to discuss the conflict. Dnipropetrovsk Governor Serhiy Lysak said the attacks that also injured tens of people came late on Wednesday, triggering multiple fires and damaging a dozen apartment buildings. A student residence, an educational institution and a food processing plant were also damaged, Dnipro Mayor Borys Filatov added. Photos posted online showed raging fires, burned-out vehicles and buildings with shattered windows and scorched facades, as emergency crews worked through the night. Sixteen of the injured are in hospital, one of them in critical condition, according to Lysak.

A Russian drone attack on the Black Sea port city of Odesa overnight injured three people, sparked fires and damaged homes and civilian infrastructure, regional governor Oleh Kiper said. Russia's Ministry of Defence said its units destroyed 26 Ukrainian drones overnight. Nine of the drones were shot down over the southern Voronezh region, while eight were taken down over the border region of Belgorod. The remaining drones were downed over the Kursk, Lipetsk and Moscow regions, as well as over the Russian-annexed Crimean peninsula, the ministry said. The Defence Ministry also said Russia has taken control of Kalynove village in Ukraine's eastern Donetsk region.

U.K. government officials have raised private concerns that Chinese-manufactured drones are being used to take high resolution images of critical national infrastructure sites in the U.K., going against guidance from the country's security services. National Grid PLC, which operates the nation's electricity and gas networks, uses drones made by Shenzhen-based SZ DJI Technology to take videos, photographs and thermal images of its electricity substations, according to information posted on its website as recently as September. DJI drones have also been used to survey the construction of Electricite de France SA's Hinkley Point C nuclear power plant, to inspect solar farms, and by Thames Water to monitor reservoirs and the water supply. Deployment of the drones comes despite a warning in 2023 by the U.K.'s National Protective Security Authority (NPSA), part of the domestic security service MI5, that British organizations managing sensitive sites should be wary of using drones "manufactured in countries with coercive data sharing practices," a reference to China. Moreover, in 2022, the U.S. Department of Defense included DJI on a blacklist of Chinese firms with military ties.

Transportation Sec. Sean Duffy indicated the Trump administration is committed to "radical transparency." In a video message about the Federal Aviation Administration doing "drone-detection testing" in New Jersey, Transportation Sec. Sean Duffy indicated that the Trump administration is committed to "radical transparency," juxtaposing that approach with what he referred to as the Biden administration's "drone fiasco." The FAA noted in a post on its website last week that the testing is slated to occur "in Cape May, New Jersey, between April 14-25." "The FAA will operate several large drones and more than 100 commercial off-the-shelf drones during the two-week period. Testing will take place over the water and near the Cape May Ferry Terminal during the daytime on weekdays only. The public should not fly recreational drones near this area during the test period," the post stated.

This paper proposes SafeGPT, a two-tiered framework that integrates generative pretrained transformers (GPTs) with reinforcement learning (RL) for efficient and reliable unmanned aerial vehicle (UAV) last-mile deliveries. In the proposed design, a Global GPT module assigns high-level tasks such as sector allocation, while an On-Device GPT manages real-time local route planning. An RL-based safety filter monitors each GPT decision and overrides unsafe actions that could lead to battery depletion or duplicate visits, effectively mitigating hallucinations. Furthermore, a dual replay buffer mechanism helps both the GPT modules and the RL agent refine their strategies over time. Simulation results demonstrate that SafeGPT achieves higher delivery success rates compared to a GPT-only baseline, while substantially reducing battery consumption and travel distance. These findings validate the efficacy of combining GPT-based semantic reasoning with formal safety guarantees, contributing a viable solution for robust and energy-efficient UAV logistics.

We present an intuitive human-drone interaction system that utilizes a gesture-based motion controller to enhance the drone operation experience in real and simulated environments. The handheld motion controller enables natural control of the drone through the movements of the operator's hand, thumb, and index finger: the trigger press manages the throttle, the tilt of the hand adjusts pitch and roll, and the thumbstick controls yaw rotation. Communication with drones is facilitated via the ExpressLRS radio protocol, ensuring robust connectivity across various frequencies. The user evaluation of the flight experience with the designed drone controller using the UEQ-S survey showed high scores for both Pragmatic (mean=2.2, SD = 0.8) and Hedonic (mean=2.3, SD = 0.9) Qualities. This versatile control interface supports applications such as research, drone racing, and training programs in real and simulated environments, thereby contributing to advances in the field of human-drone interaction.

Inspecting indoor environments such as tunnels, industrial facilities, and construction sites is essential for infrastructure monitoring and maintenance. While manual inspection in these environments is often time-consuming and potentially hazardous, Unmanned Aerial Vehicles (UAVs) can improve efficiency by autonomously handling inspection tasks. Such inspection tasks usually rely on reference maps for coverage planning. However, in industrial applications, only the floor plans are typically available. The unforeseen obstacles not included in the floor plans will result in outdated reference maps and inefficient or unsafe inspection trajectories. In this work, we propose an adaptive inspection framework that integrates global coverage planning with local reactive adaptation to improve the coverage and efficiency of UAV-based inspection in partially unknown indoor environments. Experimental results in structured indoor scenarios demonstrate the effectiveness of the proposed approach in inspection efficiency and achieving high coverage rates with adaptive obstacle handling, highlighting its potential for enhancing the efficiency of indoor facility inspection.

This paper investigates the resilience of perception-based multi-robot coordination with wireless communication to online adversarial perception. A systematic study of this problem is essential for many safety-critical robotic applications that rely on the measurements from learned perception modules. We consider a (small) team of quadrotor robots that rely only on an Inertial Measurement Unit (IMU) and the visual data measurements obtained from a learned multi-task perception module (e.g., object detection) for downstream tasks, including relative localization and coordination. We focus on a class of adversarial perception attacks that cause misclassification, mislocalization, and latency. We propose that the effects of adversarial misclassification and mislocalization can be modeled as sporadic (intermittent) and spurious measurement data for the downstream tasks. To address this, we present a framework for resilience analysis of multi-robot coordination with adversarial measurements. The framework integrates data from Visual-Inertial Odometry (VIO) and the learned perception model for robust relative localization and state estimation in the presence of adversarially sporadic and spurious measurements. The framework allows for quantifying the degradation in system observability and stability in relation to the success rate of adversarial perception. Finally, experimental results on a multi-robot platform demonstrate the real-world applicability of our methodology for resource-constrained robotic platforms.

Computer-aided design (CAD) is a promising application area for emerging artificial intelligence methods. Traditional workflows for cyberphysical systems create detailed digital models which can be evaluated by physics simulators in order to narrow the search space before creating physical prototypes. A major bottleneck of this approach is that the simulators are often computationally expensive and slow. Recent advancements in AI methods offer the possibility to accelerate these pipelines. We use the recently released AircraftVerse dataset, which is especially suited for developing and evaluating large language models for designs. AircraftVerse contains a diverse set of UAV designs represented via textual design trees together with detailed physics simulation results. Following the recent success of large language models (LLMs), we propose AGENT (Aircraft GENeraTor). AGENT is a comprehensive design tool built on the CodeT5+ LLM which learns powerful representations of aircraft textual designs directly from JSON files. We develop a curriculum of training tasks which imbues a single model with a suite of useful features. AGENT is able to generate designs conditioned on properties of flight dynamics (hover time, maximum speed, etc.). Additionally, AGENT can issue evaluations of designs allowing it to act as a surrogate model of the physics simulation that underlies the AircraftVerse dataset. We present a series of experiments which demonstrate our system's abilities. We are able to achieve strong performance using the smallest member of the CodeT5+ family (220M parameters). This allows for a flexible and powerful system which can be executed on a single GPU enabling a clear path toward future deployment.

At least 34 people were killed and another 117, including 11 children, were injured by a Russian missile attack on the northern Ukrainian city of Sumy, Ukraine's state emergency service said. This was the deadliest attack on Ukraine this year. The Ukrainian Air Force said its units intercepted and destroyed 43 of 55 Russian drones launched at Ukraine overnight. The attacks reportedly targeted the northern, southern and central areas of Ukraine. Russian forces captured the village of Yelyzavetivka in Ukraine's Donetsk region, Russia's Ministry of Defence said.