Here are the key events on day 1,226 of Russia's war on Ukraine.Smoke is seen following what local authorities called a Ukrainian drone attack, in the course of Russia-Ukraine conflict, in Sergiyev Posad, outside Moscow, Russia July 4, 2025 [Head of the Sergiyev Posad municipal district Oksana Yerokhanova via Telegram/Handout via Reuters]Published On 4 Jul 20254 Jul 2025

Friday's attacks were the latest in a string of major Russian air strikes on Ukraine that have intensified in recent weeks as ceasefire talks have largely stalled. War in Ukraine has been raging for more than three years since Russia launched its full-scale invasion in February 2022. Following his conversation with Putin on Thursday, Trump said that "no progress" to end the fighting had been made. "I'm very disappointed with the conversation I had today with President Putin, because I don't think he's there, and I'm very disappointed," Trump said. "I'm just saying I don't think he's looking to stop, and that's too bad."

While multi-vehicular collaborative driving demonstrates clear advantages over single-vehicle autonomy, traditional infrastructure-based V2X systems remain constrained by substantial deployment costs and the creation of "uncovered danger zones" in rural and suburban areas. We present AirV2X-Perception, a large-scale dataset that leverages Unmanned Aerial Vehicles (UAVs) as a flexible alternative or complement to fixed Road-Side Units (RSUs). Drones offer unique advantages over ground-based perception: complementary bird's-eye-views that reduce occlusions, dynamic positioning capabilities that enable hovering, patrolling, and escorting navigation rules, and significantly lower deployment costs compared to fixed infrastructure. Our dataset comprises 6.73 hours of drone-assisted driving scenarios across urban, suburban, and rural environments with varied weather and lighting conditions. The AirV2X-Perception dataset facilitates the development and standardized evaluation of Vehicle-to-Drone (V2D) algorithms, addressing a critical gap in the rapidly expanding field of aerial-assisted autonomous driving systems. The dataset and development kits are open-sourced at https://github.com/taco-group/AirV2X-Perception.

U.S. President Donald Trump said on Thursday that a phone call earlier in the day with Vladimir Putin resulted in no progress at all on efforts to end the war in Ukraine, while a Kremlin aide said the Russian president reiterated that Moscow would keep pushing to solve the conflict's "root causes." The two leaders did not discuss a recent pause in some U.S. weapons shipments to Kyiv during the nearly hourlong conversation, according to a readout provided by Putin aide Yuri Ushakov. U.S. attempts to end Russia's war in Ukraine through diplomacy have largely stalled, and Trump has faced growing calls -- including from some Republicans -- to increase pressure on Putin to negotiate in earnest. Within hours of the call's conclusion, an apparent Russian drone attack sparked a fire in an apartment building in a northern suburb of Kyiv, Ukrainian officials said, indicating little change in the trajectory of the conflict.

An Israeli drone attack has killed at least one person and injured three near the Lebanese capital, Beirut, the Lebanese Ministry of Public Health says, the latest violation of the ceasefire between the two countries. The air raid on Thursday hit a vehicle on a busy motorway in the Khaldeh area, about 12km (8 miles) south of Beirut. The Israeli military said it targeted "military sites and weapons depots" in the area. Bombing an area near the Lebanese capital marks another escalation by Israel, which has been carrying out near-daily bombardment in Lebanon since it reached a truce with Hezbollah in November of last year. The identities of the victims of the attack have not been released.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. The U.S. men's national team is vying for the coveted CONCACAF Gold Cup winners trophy. But, as the USMNT prepared for Wednesday's semifinal match against Guatemala, a flying object caused a disruption at the team's training grounds. An unidentified party was believed to have been operating what appeared to be a drone in the vicinity of the team's training facility in St. Louis, CBS Sports reported.

A Ukrainian drone attack on an industrial plant in Izhevsk, in central Russia, killed three people and injured 35 others, regional Governor Alexander Brechalov said in a post on Telegram. The drone struck the Kupol Electromechanical Plant, which produces air defence systems and drones for the Russian military, an unnamed official with Ukraine's Security Service, the SBU, told the Associated Press news agency. A Russian attack on a vehicle evacuating civilians from Pokrovsk, in Ukraine's Donetsk region, killed one person and injured a policeman, police said. The Ministry of Defence in Moscow said that 60 Ukrainian drones were downed overnight over several regions, including 17 over Russian-occupied Crimea, 16 over Russia's Rostov region and four over Russia's Saratov region. Ukraine's Air Force said on Tuesday that Russia launched 52 Shahed and decoy drones at the country overnight.

Optimal collision-free formation control of the unmanned aerial vehicle (UAV) is a challenge. The state-of-the-art optimal control approaches often rely on numerical methods sensitive to initial guesses. This paper presents an innovative collision-free finite-time formation control scheme for multiple UAVs leveraging the differential flatness of the UAV dynamics, eliminating the need for numerical methods. We formulate a finite-time optimal control problem to plan a formation trajectory for feasible initial states. This optimal control problem in formation trajectory planning involves a collective performance index to meet the formation requirements to achieve relative positions and velocity consensus. It is solved by applying Pontryagin's principle. Subsequently, a collision-constrained regulating problem is addressed to ensure collision-free tracking of the planned formation trajectory. The tracking problem incorporates a directionally aware collision avoidance strategy that prioritizes avoiding UAVs in the forward path and relative approach. It assigns lower priority to those on the sides with an oblique relative approach, disregarding UAVs behind and not in the relative approach. The high-fidelity simulation results validate the effectiveness of the proposed control scheme.

Low-cost indoor mobile robots have gained popularity with the increasing adoption of automation in homes and commercial spaces. However, existing lidar and camera-based solutions have limitations such as poor performance in visually obscured environments, high computational overhead for data processing, and high costs for lidars. In contrast, mmWave radar sensors offer a cost-effective and lightweight alternative, providing accurate ranging regardless of visibility. However, existing radar-based localization suffers from sparse point cloud generation, noise, and false detections. Thus, in this work, we introduce RaGNNarok, a real-time, lightweight, and generalizable graph neural network (GNN)-based framework to enhance radar point clouds, even in complex and dynamic environments. With an inference time of just 7.3 ms on the low-cost Raspberry Pi 5, RaGNNarok runs efficiently even on such resource-constrained devices, requiring no additional computational resources. We evaluate its performance across key tasks, including localization, SLAM, and autonomous navigation, in three different environments. Our results demonstrate strong reliability and generalizability, making RaGNNarok a robust solution for low-cost indoor mobile robots.

-- Recent advances in multi - agent systems manipulation have demonstrated a rising demand for the implementation of multi - UAV systems in urban areas, which are always subjected to the presence of static and dynamic obstacles. Inspired by the collective behavior of tilapia fish and pigeons, the focus of the presented research is on the introduction of a nature - inspired collision - free formation control for a multi - UAV system, considering the obstacle avoidance maneuvers. The developed framework in this study utilizes a semi - distributed control approach, in which, based on a probabilistic Lloyd's algorithm, a centralized guidance algorithm works for optimal positioning of the UAVs, while a distributed control approach has been used for the intervehicle collision and obstacle avoidance. Further, the presented framework has been extended to the 3D space with a novel definition of 3D maneuvers. Collision Avoidance, Centroidal Voronoi Tessellation, Distributed Control, Formation Control, Multi - Agent System, Obstacle Avoidance . From an engineering perspective, swarm intelligence shows how decentralized systems, composed of numerous simple agents, can achieve complex collective behaviors.