This paper introduces an autonomous UAV vision system for continuous, real-time tracking of marine animals, specifically sharks, in dynamic marine environments. The system integrates an onboard computer with a stabilised RGB-D camera and a custom-trained OSTrack pipeline, enabling visual identification under challenging lighting, occlusion, and sea-state conditions. A key innovation is the inter-UAV handoff protocol, which enables seamless transfer of tracking responsibilities between drones, extending operational coverage beyond single-drone battery limitations. Performance is evaluated on a curated shark dataset of 5,200 frames, achieving a tracking success rate of 81.9\% during real-time flight control at 100 Hz, and robustness to occlusion, illumination variation, and background clutter. We present a seamless UAV handoff framework, where target transfer is attempted via high-confidence feature matching, achieving 82.9\% target coverage. These results confirm the viability of coordinated UAV operations for extended marine tracking and lay the groundwork for scalable, autonomous monitoring.

A Russian air raid on a shopping centre and market in Dobropillia, eastern Ukraine, killed at least two people, wounded 22 others and caused widespread damage on Wednesday, the regional governor, Vadym Filashkin, said. Filashkin said the building was struck by a 500kg (1,100-pound) bomb at 5:20pm (14:20 GMT). Russia launched 400 Shahed and decoy drones, as well as one ballistic missile, on Wednesday night, the Ukrainian air force said. The strikes targeted the northeastern city of Kharkiv, the central city of Kryvyi Rih, Vinnytsia in the west, and Odesa in the south. A Ukrainian drone killed one person and injured six others in the Russian city of Belgorod, and injured one person in a village northeast of the city, the regional governor, Vyacheslav Gladkov, said.

Sidewalk delivery robots are a promising solution for urban freight distribution, reducing congestion compared to trucks and providing a safer, higher-capacity alternative to drones. However, unreliable travel times on sidewalks due to pedestrian density, obstacles, and varying infrastructure conditions can significantly affect their efficiency. This study addresses the robust route planning problem for sidewalk robots, explicitly accounting for travel time uncertainty due to varying sidewalk conditions. Optimization is integrated with simulation to reproduce the effect of obstacles and pedestrian flows and generate realistic travel times. The study investigates three different approaches to derive uncertainty sets, including budgeted, ellipsoidal, and support vector clustering (SVC)-based methods, along with a distributionally robust method to solve the shortest path (SP) problem. A realistic case study reproducing pedestrian patterns in Stockholm's city center is used to evaluate the efficiency of robust routing across various robot designs and environmental conditions. The results show that, when compared to a conventional SP, robust routing significantly enhances operational reliability under variable sidewalk conditions. The Ellipsoidal and DRSP approaches outperform the other methods, yielding the most efficient paths in terms of average and worst-case delay. Sensitivity analyses reveal that robust approaches consistently outperform the conventional SP, particularly for sidewalk delivery robots that are wider, slower, and have more conservative navigation behaviors. These benefits are even more pronounced in adverse weather conditions and high pedestrian congestion scenarios.

At least fifteen people have been injured in the latest wave of Russian drone strikes, which this time hit four Ukranian cities. Video shows firefighters battling flames from a large blaze. This comes amid the 50-day deadline from the US for Russia to secure a peace deal.

An NYPD drone captured four minors between the ages of 12 and 16 riding on top of a train in the Bronx Thursday as it passed multiple stations at a high speed. Three teenagers and one 12-year-old boy were apprehended by police after an NYPD drone captured them riding on top of a train in New York City Thursday passing through multiple stations at a high speed. NYPD drone footage obtained by Fox News Digital shows the four subway surfers -- between the ages of 12 and 16 -- climbing up the side of the moving northbound 6 express train as it passed beneath the Westchester Avenue Bridge. The minors can then be seen standing up and forming a line, some of them jumping up and down and spreading their arms. NYPD drone footage obtained by Fox News Digital shows the four subway surfers -- between the ages of 12 and 16 -- climbing up the side of the moving northbound 6 express train as it passed beneath the Westchester Avenue Bridge.

Vision is an essential part of attitude control for many flying animals, some of which have no dedicated sense of gravity. Flying robots, on the other hand, typically depend heavily on accelerometers and gyroscopes for attitude stabilization. In this work, we present the first vision-only approach to flight control for use in generic environments. We show that a quadrotor drone equipped with a downward-facing event camera can estimate its attitude and rotation rate from just the event stream, enabling flight control without inertial sensors. Our approach uses a small recurrent convolutional neural network trained through supervised learning. Real-world flight tests demonstrate that our combination of event camera and low-latency neural network is capable of replacing the inertial measurement unit in a traditional flight control loop. Furthermore, we investigate the network's generalization across different environments, and the impact of memory and different fields of view. While networks with memory and access to horizon-like visual cues achieve best performance, variants with a narrower field of view achieve better relative generalization. Our work showcases vision-only flight control as a promising candidate for enabling autonomous, insect-scale flying robots.

Autonomous unmanned aerial vehicles (UAVs) rely on global navigation satellite system (GNSS) pseudorange measurements for accurate real-time localization and navigation. However, this dependence exposes them to sophisticated spoofing threats, where adversaries manipulate pseudoranges to deceive UAV receivers. Among these, drift-evasive spoofing attacks subtly perturb measurements, gradually diverting the UAVs trajectory without triggering conventional signal-level anti-spoofing mechanisms. Traditional distributional shift detection techniques often require accumulating a threshold number of samples, causing delays that impede rapid detection and timely response. Consequently, robust temporal-scale detection methods are essential to identify attack onset and enable contingency planning with alternative sensing modalities, improving resilience against stealthy adversarial manipulations. This study explores a Bayesian online change point detection (BOCPD) approach that monitors temporal shifts in value estimates from a reinforcement learning (RL) critic network to detect subtle behavioural deviations in UAV navigation. Experimental results show that this temporal value-based framework outperforms conventional GNSS spoofing detectors, temporal semi-supervised learning frameworks, and the Page-Hinkley test, achieving higher detection accuracy and lower false-positive and false-negative rates for drift-evasive spoofing attacks.

In multiple unmanned ground vehicle confrontations, autonomously evolving multi-agent tactical decisions from situational awareness remain a significant challenge. Traditional handcraft rule-based methods become vulnerable in the complicated and transient battlefield environment, and current reinforcement learning methods mainly focus on action manipulation instead of strategic decisions due to lack of interpretability. Here, we propose a vision-language model-based commander to address the issue of intelligent perception-to-decision reasoning in autonomous confrontations. Our method integrates a vision language model for scene understanding and a lightweight large language model for strategic reasoning, achieving unified perception and decision within a shared semantic space, with strong adaptability and interpretability. Unlike rule-based search and reinforcement learning methods, the combination of the two modules establishes a full-chain process, reflecting the cognitive process of human commanders. Simulation and ablation experiments validate that the proposed approach achieves a win rate of over 80% compared with baseline models.

This paper presents a multi-agent reinforcement learning (MARL) framework for cooperative collision avoidance of UA V swarms leveraging domain knowledge-driven reward. The reward is derived from knowledge in the domain of image processing, approximating contours on a two-dimensional field. By modeling obstacles as maxima on the field, collisions are inherently avoided as contours never go through peaks or intersect. Additionally, counters are smooth and energy-efficient. Our framework enables training with large swarm sizes as the agent interaction is minimized and the need for complex credit assignment schemes or observation sharing mechanisms in state-of-the-art MARL approaches are eliminated. Moreover, UA Vs obtain the ability to adapt to complex environments where contours may be nonviable or non-existent through intensive training. Extensive experiments are conducted to evaluate the performances of our framework against state-of-the-art MARL algorithms.

The rapid development of urban low-altitude unmanned aerial vehicle (UAV) economy poses new challenges for dynamic site selection of UAV landing points and supply stations. Traditional deep reinforcement learning methods face computational complexity bottlenecks, particularly with standard attention mechanisms, when handling large-scale urban-level location problems. This paper proposes GeoHopNet, a Hopfield-augmented sparse spatial attention network specifically designed for dynamic UAV site location problems. Our approach introduces four core innovations: (1) distance-biased multi-head attention mechanism that explicitly encodes spatial geometric information; (2) K-nearest neighbor sparse attention that reduces computational complexity from $O(N^2)$ to $O(NK)$; (3) a modern Hopfield external memory module; and (4) a memory regularization strategy. Experimental results demonstrate that GeoHopNet extends the boundary of solvable problem sizes. For large-scale instances with 1,000 nodes, where standard attention models become prohibitively slow (over 3 seconds per instance) and traditional solvers fail, GeoHopNet finds high-quality solutions (0.22\% optimality gap) in under 0.1 seconds. Compared to the state-of-the-art ADNet baseline on 100-node instances, our method improves solution quality by 22.2\% and is 1.8$\times$ faster.