Fox News contributors Katie Pavlich and Miranda Devine discuss how President Donald Trump could be the one to bring an end to the Russia-Ukraine war on'Hannity.' Rep. Marjorie Taylor Greene, R-Ga., late Thursday took shots at Ukrainian President Volodymyr Zelenskyy, accusing him of trying to sabotage Friday's highly anticipated peace talks between President Donald Trump and Russian President Vladimir Putin by launching drone strikes on Russia. Greene responded to a post on X from the account, "Open Source Intel," which reported that Ukraine had in recent hours launched "one of the largest" drone attacks on Russia. "On the eve of the historic peace talks between President Trump and President Putin, Zelensky does this," the Republican lawmaker wrote. "Zelensky doesn't want peace and obviously is trying to sabotage President Trump's heroic efforts to end the war in Ukraine. Fox News Digital has reached out to the Ukrainian embassy, seeking a response to Greene's post. Rep. Marjorie Taylor Greene, R-Ga., accused Ukrainian President Zelenskyy of trying to sabotage peace talks between President Trump and Russian President Putin by launching drone strikes on Russia. Ukraine launched multiple drone strikes into Russia overnight Thursday, damaging several apartment buildings in the southern city of Rostov-on-Don and injuring more than a dozen civilians, according to acting governor of the region, Yuri Slyusar. Two of those wounded were hospitalized in serious condition, he said. The Ukrainian strikes came after Russian strikes in Ukraine's Sumy region overnight Wednesday, resulting in multiple injuries, including a 7-year-old girl, per officials. Local officials also accused Ukraine of launching a drone strike in Belgorod that injured three people, and another that struck a car in the village of Pristen that killed at least one individual. Ukrainian President Volodymyr Zelenskyy will not attend the summit in Alaska on Friday between President Donald Trump and Russian President Vladimir Putin. Despite the violence, Trump and Putin are scheduled to meet in Anchorage, Alaska, on Friday for a high-stakes summit on the future of the Ukraine war. The meeting will mark Putin's first visit to the U.S. since 2015 and the first U.S.-Russia summit since June 2021. President Donald Trump will meet with Russian President Vladimir Putin in Alaska on Aug. 15, 2025. Putin praised the U.S. on Thursday for making "sincere efforts" to end the war between Russia and Ukraine, which has been raging since early 2022. Appearing on television, the Russian president said the U.S. was "making, in my opinion, quite energetic and sincere efforts to stop hostilities, stop the crisis and reach agreements that are of interest to all parties involved in this conflict." Zelenskyy accused Russia of not being sincere in its intention to wind down the war. "This war must be ended.

New video shows the moment a Ukrainian drone struck an apartment building in Rostov-on-Don, Russia. Officials say at least 13 people were injured in the attack.

Collective behaviors such as swarming and flocking emerge from simple, decentralized interactions in biological systems. Existing models, such as Vicsek and Cucker-Smale, lack collision avoidance, whereas the Olfati-Saber model imposes rigid formations, limiting their applicability in swarm robotics. To address these limitations, this paper proposes a minimal yet expressive model that governs agent dynamics using relative positions, velocities, and local density, modulated by two tunable parameters: the spatial offset and kinetic offset. The model achieves spatially flexible, collision-free behaviors that reflect naturalistic group dynamics. Furthermore, we extend the framework to cognitive autonomous systems, enabling energy-aware phase transitions between swarming and flocking through adaptive control parameter tuning. This cognitively inspired approach offers a robust foundation for real-world applications in multi-robot systems, particularly autonomous aerial swarms.

A great white shark was spotted this week swimming in the area of Scarborough, Maine. A drone video captured a great white shark lurking in the waters of a vacation spot in the Northeast. Police in Scarborough, Maine, which is located just south of Portland, confirmed this week that the shark was spotted off the state's coastline. "On Monday, August 11, 2025, Scarborough's Marine Resource Officer received a report of what appeared to be a large shark near Richmond Island and Scarborough Beach," the town wrote on its Facebook page. "Follow-up observations were conducted, and on Tuesday, August 12, 2025, the Marine Resource Officer obtained drone video footage showing a possible great white shark, estimated to be 10–12 feet in length, off the southern end of Richmond Island in the vicinity of Higgins Beach and Scarborough Beach," it added.

Unmanned Aerial Vehicles (UAVs) have made significant advancements in communication stability and security through techniques such as frequency hopping, signal spreading, and adaptive interference suppression. However, challenges remain in modeling spectrum competition, integrating expert knowledge, and predicting opponent behavior. To address these issues, we propose UAV-FPG (Unmanned Aerial Vehicle - Frequency Point Game), a game-theoretic environment model that simulates the dynamic interaction between interference and anti-interference strategies of opponent and ally UAVs in communication frequency bands. The model incorporates a prior expert knowledge base to optimize frequency selection and employs large language models for path planning, simulating a "strong adversary". Experimental results highlight the effectiveness of integrating the expert knowledge base and the large language model, with the latter significantly improving path planning in dynamic scenarios through iterative interactions, outperforming fixed-path strategies. UAV-FPG provides a robust platform for advancing anti-jamming strategies and intelligent decision-making in UAV communication systems.

A mysterious black cube has joined the chilling list of objects spotted hovering over the US during last year's drone invasion. Newly released government reports have revealed five incidents near Wright-Patterson Air Force Base in Ohio that have never been disclosed since the swarms of UFOs were seen along the East Coast in late 2024. Along with several sightings of unidentified drones around the secretive Air Force base in December 2024, federal officials now say a'black cube'-shaped craft was spotted by a nearby airplane less than 80 miles from Wright-Patterson. Witnesses of the strange object sent their claims to the Federal Aviation Administration (FAA) on December 19, describing how the cube was flying within 500 feet of the plane, which was soaring 16,000 feet above the ground. This would make it incredibly unlikely to be a commercial drone, since those types of devices fly only a few hundred feet above the ground.

This paper introduces an advanced AI-driven perception system for autonomous quadcopter navigation in GPS-denied indoor environments. The proposed framework leverages cloud computing to offload computationally intensive tasks and incorporates a custom-designed printed circuit board (PCB) for efficient sensor data acquisition, enabling robust navigation in confined spaces. The system integrates YOLOv11 for object detection, Depth Anything V2 for monocular depth estimation, a PCB equipped with Time-of-Flight (ToF) sensors and an Inertial Measurement Unit (IMU), and a cloud-based Large Language Model (LLM) for context-aware decision-making. A virtual safety envelope, enforced by calibrated sensor offsets, ensures collision avoidance, while a multithreaded architecture achieves low-latency processing. Enhanced spatial awareness is facilitated by 3D bounding box estimation with Kalman filtering. Experimental results in an indoor testbed demonstrate strong performance, with object detection achieving a mean Average Precision (mAP50) of 0.6, depth estimation Mean Absolute Error (MAE) of 7.2 cm, only 16 safety envelope breaches across 42 trials over approximately 11 minutes, and end-to-end system latency below 1 second. This cloud-supported, high-intelligence framework serves as an auxiliary perception and navigation system, complementing state-of-the-art drone autonomy for GPS-denied confined spaces.

With the growing demand for efficient logistics, unmanned aerial vehicles (UAVs) are increasingly being paired with automated guided vehicles (AGVs). While UAVs offer the ability to navigate through dense environments and varying altitudes, they are limited by battery life, payload capacity, and flight duration, necessitating coordinated ground support. Focusing on heterogeneous navigation, SwarmVLM addresses these limitations by enabling semantic collaboration between UAVs and ground robots through impedance control. The system leverages the Vision Language Model (VLM) and the Retrieval-Augmented Generation (RAG) to adjust impedance control parameters in response to environmental changes. In this framework, the UAV acts as a leader using Artificial Potential Field (APF) planning for real-time navigation, while the ground robot follows via virtual impedance links with adaptive link topology to avoid collisions with short obstacles. The system demonstrated a 92% success rate across 12 real-world trials. Under optimal lighting conditions, the VLM-RAG framework achieved 8% accuracy in object detection and selection of impedance parameters. The mobile robot prioritized short obstacle avoidance, occasionally resulting in a lateral deviation of up to 50 cm from the UAV path, which showcases safe navigation in a cluttered setting.

Fleets of autonomous robots are increasingly deployed alongside multiple human operators to explore unknown environments, identify salient features, and perform complex tasks in scenarios such as subterranean exploration, reconnaissance, and search-and-rescue missions. In these contexts, communication is often severely limited to short-range exchanges via ad-hoc networks, posing challenges to coordination. While recent studies have addressed multi-robot exploration under communication constraints, they largely overlook the essential role of human operators and their real-time interaction with robotic teams. Operators may demand timely updates on the exploration progress and robot status, reprioritize or cancel tasks dynamically, or request live video feeds and control access. Conversely, robots may seek human confirmation for anomalous events or require help recovering from motion or planning failures. To enable such bilateral, context-aware interactions under restricted communication, this work proposes MoRoCo, a unified framework for online coordination and exploration in multi-operator, multi-robot systems. MoRoCo enables the team to adaptively switch among three coordination modes: spread mode for parallelized exploration with intermittent data sharing, migrate mode for coordinated relocation, and chain mode for maintaining high-bandwidth connectivity through multi-hop links. These transitions are managed through distributed algorithms via only local communication. Extensive large-scale human-in-the-loop simulations and hardware experiments validate the necessity of incorporating human robot interactions and demonstrate that MoRoCo enables efficient, reliable coordination under limited communication, marking a significant step toward robust human-in-the-loop multi-robot autonomy in challenging environments.

Field trials are vital in herbicide research and development to assess effects on crops and weeds under varied conditions. Traditionally, evaluations rely on manual visual assessments, which are time-consuming, labor-intensive, and subjective. Automating species and damage identification is challenging due to subtle visual differences, but it can greatly enhance efficiency and consistency. We present an improved segmentation model combining a general-purpose self-supervised visual model with hierarchical inference based on botanical taxonomy. Trained on a multi-year dataset (2018-2020) from Germany and Spain using digital and mobile cameras, the model was tested on digital camera data (year 2023) and drone imagery from the United States, Germany, and Spain (year 2024) to evaluate robustness under domain shift. This cross-device evaluation marks a key step in assessing generalization across platforms of the model. Our model significantly improved species identification (F1-score: 0.52 to 0.85, R-squared: 0.75 to 0.98) and damage classification (F1-score: 0.28 to 0.44, R-squared: 0.71 to 0.87) over prior methods. Under domain shift (drone images), it maintained strong performance with moderate degradation (species: F1-score 0.60, R-squared 0.80; damage: F1-score 0.41, R-squared 0.62), where earlier models failed. These results confirm the model's robustness and real-world applicability. It is now deployed in BASF's phenotyping pipeline, enabling large-scale, automated crop and weed monitoring across diverse geographies.