In various scenarios such as multi-drone surveillance and search-and-rescue operations, deploying multiple robots is essential to accomplish multiple tasks at once. Due to the limited communication range of these vehicles, a decentralised task allocation algorithm is crucial for effective task distribution among robots. The consensus-based bundle algorithm (CBBA) has been promising for multi-robot operation, offering theoretical guarantees. However, CBBA demands continuous communication, leading to potential congestion and packet loss that can hinder performance. In this study, we introduce an event-driven communication mechanism designed to address these communication challenges while maintaining the convergence and performance bounds of CBBA. We demonstrate theoretically that the solution quality matches that of CBBA and validate the approach with Monte-Carlo simulations across varying targets, agents, and bundles. Results indicate that the proposed algorithm (ED-CBBA) can reduce message transmissions by up to 52%.

The You Only Look Once (YOLO) architecture is crucial for real-time object detection. However, deploying it in resource-constrained environments such as unmanned aerial vehicles (UAVs) requires efficient transfer learning. Although layer freezing is a common technique, the specific impact of various freezing configurations on contemporary YOLOv8 and YOLOv10 architectures remains unexplored, particularly with regard to the interplay between freezing depth, dataset characteristics, and training dynamics. This research addresses this gap by presenting a detailed analysis of layer-freezing strategies. We systematically investigate multiple freezing configurations across YOLOv8 and YOLOv10 variants using four challenging datasets that represent critical infrastructure monitoring. Our methodology integrates a gradient behavior analysis (L2 norm) and visual explanations (Grad-CAM) to provide deeper insights into training dynamics under different freezing strategies. Our results reveal that there is no universal optimal freezing strategy but, rather, one that depends on the properties of the data. For example, freezing the backbone is effective for preserving general-purpose features, while a shallower freeze is better suited to handling extreme class imbalance. These configurations reduce graphics processing unit (GPU) memory consumption by up to 28% compared to full fine-tuning and, in some cases, achieve mean average precision (mAP@50) scores that surpass those of full fine-tuning. Gradient analysis corroborates these findings, showing distinct convergence patterns for moderately frozen models. Ultimately, this work provides empirical findings and practical guidelines for selecting freezing strategies. It offers a practical, evidence-based approach to balanced transfer learning for object detection in scenarios with limited resources.

Traditional fixed architectures struggle to cope with dynamic and unpredictable environments, leading to inefficiencies in energy consumption and connectivity. This paper addresses this gap by proposing an adaptive architecture for drone swarms, leveraging a Large Language Model (LLM) to dynamically select the optimal architecture--centralized, hierarchical, or holonic--based on real-time mission parameters such as task complexity, swarm size, and communication stability. Our system addresses the challenges of scalability, adaptability, and robustness, ensuring efficient energy consumption and maintaining connectivity under varying conditions. Extensive simulations demonstrate that our adaptive architecture outperforms traditional static models in terms of scalability, energy efficiency, and connectivity. These results highlight the potential of our approach to provide a scalable, adaptable, and resilient solution for real-world disaster response scenarios.

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What does survival look like inside Gaza City? 'How to stop Israel from starving Gaza' Video shows the moment an Israeli drone laden with explosives was flown into the minaret of a mosque in Deir el-Balah, central Gaza. Israel wants to'destroy Gaza City, not occupy it'

In this paper, we propose a 3D path planning method that integrates the A* algorithm with the octree structure. Unmanned Ground Vehicles (UGVs) and legged robots have been extensively studied, enabling locomotion across a variety of terrains. Advances in mobility have enabled obstacles to be regarded not only as hindrances to be avoided, but also as navigational aids when beneficial. A modified 3D A* algorithm generates an optimal path by leveraging obstacles during the planning process. By incorporating a height-based penalty into the cost function, the algorithm enables the use of traversable obstacles to aid locomotion while avoiding those that are impassable, resulting in more efficient and realistic path generation. The octree-based 3D grid map achieves compression by merging high-resolution nodes into larger blocks, especially in obstacle-free or sparsely populated areas. This reduces the number of nodes explored by the A* algorithm, thereby improving computational efficiency and memory usage, and supporting real-time path planning in practical environments. Benchmark results demonstrate that the use of octree structure ensures an optimal path while significantly reducing memory usage and computation time.

Traffic congestion and violations pose significant challenges for urban mobility and road safety. Traditional traffic monitoring systems, such as fixed cameras and sensor-based methods, are often constrained by limited coverage, low adaptability, and poor scalability. To address these challenges, this paper introduces an advanced unmanned aerial vehicle (UAV)-based traffic surveillance system capable of accurate vehicle detection, classification, tracking, and behavioral analysis in real-world, unconstrained urban environments. The system leverages multi-scale and multi-angle template matching, Kalman filtering, and homography-based calibration to process aerial video data collected from altitudes of approximately 200 meters. A case study in urban area demonstrates robust performance, achieving a detection precision of 91.8%, an F1-score of 90.5%, and tracking metrics (MOTA/MOTP) of 92.1% and 93.7%, respectively. Beyond precise detection, the system classifies five vehicle types and automatically detects critical traffic violations, including unsafe lane changes, illegal double parking, and crosswalk obstructions, through the fusion of geofencing, motion filtering, and trajectory deviation analysis. The integrated analytics module supports origin-destination tracking, vehicle count visualization, inter-class correlation analysis, and heatmap-based congestion modeling. Additionally, the system enables entry-exit trajectory profiling, vehicle density estimation across road segments, and movement direction logging, supporting comprehensive multi-scale urban mobility analytics. Experimental results confirms the system's scalability, accuracy, and practical relevance, highlighting its potential as an enforcement-aware, infrastructure-independent traffic monitoring solution for next-generation smart cities.

We present a new approach for designing risk-bounded controllers for Uncrewed Aerial Vehicles (UAVs). Existing frameworks for assessing risk of UAV operations rely on knowing the conditional probability of an incident occurring given different causes. Limited data for computing these probabilities makes real-world implementation of these frameworks difficult. Furthermore, existing frameworks do not include control methods for risk mitigation. Our approach relies on UAV dynamics, and employs reachability analysis for a probabilistic risk assessment over all feasible UAV trajectories. We use this holistic risk assessment to formulate a control optimization problem that minimally changes a UAV's existing control law to be bounded by an accepted risk threshold. We call our approach PRReach. Public and readily available UAV dynamics models and open source spatial data for mapping hazard outcomes enables practical implementation of PRReach for both offline pre-flight and online in-flight risk assessment and mitigation. We evaluate PRReach through simulation experiments on real-world data. Results show that PRReach controllers reduce risk by up to 24% offline, and up to 53% online from classical controllers.

Fox News chief political analyst Brit Hume explains why President Donald Trump should not remove himself from the peace negotiations between Russia and Ukraine and more on'Special Report.' When Russia invaded Ukraine in February 2022, many experts predicted Kyiv's quick fall. When Ukraine pushed back overextended Russian forces, the same experts confidently said that Russia's mass -- a population almost four times larger than Ukraine -- would certainly grind Ukraine down. Triumph for Putin was inevitable. But, an odd thing happened on the way to Russia's victory parade: Ukraine is outfighting Russia.

At least three people have been killed, 18 wounded, and dozens of buildings set on fire in Kyiv, including the seat of the government, following a Russian drone and missile attack in Ukraine's capital, according to officials and local news reports. Kyiv Mayor Vitali Klitschko was initially quoted by Reuters news agency as saying that the attack early on Sunday killed an infant and a young woman, and sparked fires at several high-rise apartment buildings in the city's west and east. Medics were called to the leafy Darnytskyi district to the east of the Dnipro River, where a four-storey apartment building caught fire from the debris of drones destroyed in the overnight attack, Klitschko added. The Ukrainian news website Kyiv Independent also reported that an elderly woman also died in a shelter in the city's Darnytskyi district following the attack, although the cause of of her death was not immediately clear. The State Emergency Service confirmed at least one fatality in Kyiv and at least 18 injured.