Russia's Defence Ministry said air defences shot down 105 Ukrainian drones over Russian regions, including 35 over the Moscow region, after the ministry said a day earlier that it had downed more than 300 Ukrainian drones. Kherson Governor Oleksandr Prokudin said one person was killed in a Russian artillery attack on the region. H said over the past day, 35 areas in Kherson, including Kherson city, came under artillery shelling and air attacks, wounding 11 people. Ukrainian President Zelenskyy said the "most intense situation" is in the Donetsk region, and the army is continuing "active operations in the Kursk and Belgorod regions". Russia's Defence Ministry said air defences shot down 105 Ukrainian drones over Russian regions, including 35 over the Moscow region, after the ministry said a day earlier that it had downed more than 300 Ukrainian drones.

--Uncrewed Aerial V ehicles (UA Vs) have become a focal point of research, with both established companies and startups investing heavily in their development. This paper presents our iterative process in developing a robust autonomous marker-based landing system, highlighting the key challenges encountered and the solutions implemented. It reviews existing systems for autonomous landing processes, and through this aims to contribute to the community by sharing insights and challenges faced during development and testing. Autonomous landing of Uncrewed Aerial V ehicles (UA Vs) represents a critical and core aspect for developing the reliability and safety of UA V operations and paves the way for more complex and ambitious applications of drone technology in both civilian and military domains. Applications such as package delivery services [1] and infrastructure inspections [2] benefit from improved landing systems. Autonomous landing systems can be broadly categorised into two types: marker-based [3] and marker-less [4] .

Drones have become essential in various applications, but conventional quadrotors face limitations in confined spaces and complex tasks. Deformable drones, which can adapt their shape in real-time, offer a promising solution to overcome these challenges, while also enhancing maneuverability and enabling novel tasks like object grasping. This paper presents a novel approach to autonomous motion planning and control for deformable quadrotors. We introduce a shape-adaptive trajectory planner that incorporates deformation dynamics into path generation, using a scalable kinodynamic A* search to handle deformation parameters in complex environments. The backend spatio-temporal optimization is capable of generating optimally smooth trajectories that incorporate shape deformation. Additionally, we propose an enhanced control strategy that compensates for external forces and torque disturbances, achieving a 37.3\% reduction in trajectory tracking error compared to our previous work. Our approach is validated through simulations and real-world experiments, demonstrating its effectiveness in narrow-gap traversal and multi-modal deformable tasks.

A routine training flight over Arizona in January 2023 took an unusual turn when a U.S. Air Force F-16D was struck by what was initially reported as an unidentified object, but now U.S. defense officials say was a small drone. Fox News confirmed that the incident, which occurred near Gila Bend, Arizona, on Jan. 19, 2023, was a routine training mission and was witnessed by the instructor pilot seated in the rear of the two-seat aircraft. According to a U.S. defense official, the pilot observed a "mostly white and orange object" collide with the left side of the aircraft canopy, the transparent covering over the cockpit. Initially, the object was thought to be a bird, a common hazard for aircraft. But after conducting checks during the flight and a detailed inspection upon landing at Tucson International Airport, the crew found "zero evidence" of a bird strike.

In this paper, a novel quantum classical hybrid framework is proposed that synergizes quantum with Classical Reinforcement Learning. By leveraging the inherent parallelism of quantum computing, the proposed approach generates robust Q tables and specialized turn cost estimations, which are then integrated with a classical Reinforcement Learning pipeline. The Classical Quantum fusion results in rapid convergence of training, reducing the training time significantly and improved adaptability in scenarios featuring static, dynamic, and moving obstacles. Simulator based evaluations demonstrate significant enhancements in path efficiency, trajectory smoothness, and mission success rates, underscoring the potential of framework for real time, autonomous navigation in complex and unpredictable environments. Furthermore, the proposed framework was tested beyond simulations on practical scenarios, including real world map data such as the IIT Delhi campus, reinforcing its potential for real time, autonomous navigation in complex and unpredictable environments.

A UFO slammed into a US fighter jet over Arizona, cracking the canopy protecting the pilot, and forcing the 63 million plane to land, new reports have revealed. According to the Federal Aviation Administration (FAA), the F-16 Viper fighter jet was hit by an'orange-white UAS' - which stands for uncrewed aerial system, better known as a drone - on January 19, 2023. Within a day of this collision, there were three more unidentified aircraft sightings over the Air Force's Barry Goldwater Range, where the fighter was damaged, the documents stated. Barry Goldwater Range is an expanse of desert along the Arizona-Mexico border where the military practices air-to-air and air-to-ground combat. The FAA's report of the F-16 collision revealed that the fighter was flying in restricted airspace near Gila Bend, Arizona, when it was hit by the object in the rear of the canopy, the glass bubble which protects the pilot.

Sim-to-real transfer is a fundamental challenge in robot reinforcement learning. Discrepancies between simulation and reality can significantly impair policy performance, especially if it receives high-dimensional inputs such as dense depth estimates from vision. We propose a novel depth transfer method based on domain adaptation to bridge the visual gap between simulated and real-world depth data. A Variational Autoencoder (VAE) is first trained to encode ground-truth depth images from simulation into a latent space, which serves as input to a reinforcement learning (RL) policy. During deployment, the encoder is refined to align stereo depth images with this latent space, enabling direct policy transfer without fine-tuning. We apply our method to the task of autonomous drone navigation through cluttered environments. Experiments in IsaacGym show that our method nearly doubles the obstacle avoidance success rate when switching from ground-truth to stereo depth input. Furthermore, we demonstrate successful transfer to the photo-realistic simulator AvoidBench using only IsaacGym-generated stereo data, achieving superior performance compared to state-of-the-art baselines. Real-world evaluations in both indoor and outdoor environments confirm the effectiveness of our approach, enabling robust and generalizable depth-based navigation across diverse domains.

Unmanned Aerial Vehicle (UAV) Vision-and-Language Navigation (VLN) is vital for applications such as disaster response, logistics delivery, and urban inspection. However, existing methods often struggle with insufficient multimodal fusion, weak generalization, and poor interpretability. To address these challenges, we propose FlightGPT, a novel UAV VLN framework built upon Vision-Language Models (VLMs) with powerful multimodal perception capabilities. We design a two-stage training pipeline: first, Supervised Fine-Tuning (SFT) using high-quality demonstrations to improve initialization and structured reasoning; then, Group Relative Policy Optimization (GRPO) algorithm, guided by a composite reward that considers goal accuracy, reasoning quality, and format compliance, to enhance generalization and adaptability. Furthermore, FlightGPT introduces a Chain-of-Thought (CoT)-based reasoning mechanism to improve decision interpretability. Extensive experiments on the city-scale dataset CityNav demonstrate that FlightGPT achieves state-of-the-art performance across all scenarios, with a 9.22\% higher success rate than the strongest baseline in unseen environments. Our implementation is publicly available.

Amid a serious truck driver shortage, convenience store chain Seven-Eleven Japan began a trial delivery service using robots on public roads in a western Tokyo suburb on Monday. In the experimental project involving two stores in the city of Hachioji, two robots at each outlet carry items ordered through the 7NOW delivery service app. The four-wheeled box-type robots, which can travel up to 6 kph, are designed to run on sidewalks while following traffic lights and dodging obstacles. After conducting the tests until February next year, Seven-Eleven Japan will consider the feasibility of the robot delivery service, which is expected to help the company cope with a driver shortage and better serve older customers who have difficulty going out shopping.

--Ensuring robust and real-time obstacle avoidance is critical for the safe operation of autonomous robots in dynamic, real-world environments. This paper proposes a neural network framework for predicting the time and collision position of an unmanned aerial vehicle with a dynamic object, using RGB and event-based vision sensors. The proposed architecture consists of two separate encoder branches, one for each modality, followed by fusion by self-attention to improve prediction accuracy. T o facilitate benchmarking, we leverage the ABCD [8] dataset collected that enables detailed comparisons of single-modality and fusion-based approaches. At the same prediction throughput of 50Hz, the experimental results show that the fusion-based model offers an improvement in prediction accuracy over single-modality approaches of 1% on average and 10% for distances beyond 0.5m, but comes at the cost of +71% in memory and + 105% in FLOPs. Notably, the event-based model outperforms the RGB model by 4% for position and 26% for time error at a similar computational cost, making it a competitive alternative. Additionally, we evaluate quantized versions of the event-based models, applying 1-to 8-bit quantization to assess the trade-offs between predictive performance and computational efficiency.