We introduce SRDrone, a novel system designed for self-refinement task planning in industrial-grade embodied drones. SRDrone incorporates two key technical contributions: First, it employs a continuous state evaluation methodology to robustly and accurately determine task outcomes and provide explanatory feedback. This approach supersedes conventional reliance on single-frame final-state assessment for continuous, dynamic drone operations. Second, SRDrone implements a hierarchical Behavior Tree (BT) modification model. This model integrates multi-level BT plan analysis with a constrained strategy space to enable structured reflective learning from experience. Experimental results demonstrate that SRDrone achieves a 44.87% improvement in Success Rate (SR) over baseline methods. Furthermore, real-world deployment utilizing an experience base optimized through iterative self-refinement attains a 96.25% SR. By embedding adaptive task refinement capabilities within an industrial-grade BT planning framework, SRDrone effectively integrates the general reasoning intelligence of Large Language Models (LLMs) with the stringent physical execution constraints inherent to embodied drones. Code is available at https://github.com/ZXiiiC/SRDrone.

At least one person has been killed and 18 others wounded in a Russian drone and missile attack on Ukraine, officials said, as Moscow launched its largest attack on its neighbour in weeks amid an ongoing diplomatic push for a ceasefire. Russian forces launched 574 drones and 40 missiles overnight, Ukraine's Air Force said on Thursday, adding that its air defence units had downed most of the attacks. But a number of the attacks struck targets in several locations across Ukraine, resulting in casualties and damage to buildings. In the western city of Lviv, about 70km (43 miles) from the border with Poland, a drone and missile attack killed one person, injured three and damaged 26 residential buildings, Governor Maksym Kozytskyi said. In Mukachevo, near the border with Hungary and Slovakia, 15 people were wounded in Russian attacks, local authorities said.

--Despite extensive developments in motion planning of autonomous aerial vehicles (AA Vs), existing frameworks faces the challenges of local minima and deadlock in complex dynamic environments, leading to increased collision risks. T o address these challenges, we present TRUST -Planner, a topology-guided hierarchical planning framework for robust spatial-temporal obstacle avoidance. In the frontend, a dynamic enhanced visible probabilistic roadmap (DEV-PRM) is proposed to rapidly explore topological paths for global guidance. The backend utilizes a uniform terminal-free minimum control polynomial (UTF-MINCO) and dynamic distance field (DDF) to enable efficient predictive obstacle avoidance and fast parallel computation. Furthermore, an incremental multi-branch trajectory management framework is introduced to enable spatio-temporal topological decision-making, while efficiently leveraging historical information to reduce replanning time. Simulation results show that TRUST - Planner outperforms baseline competitors, achieving a 96% success rate and millisecond-level computation efficiency in tested complex environments. Real-world experiments further validate the feasibility and practicality of the proposed method. URRENTL Y, autonomous aerial vehicles (AA Vs) play an increasingly crucial role in widespread areas, e.g., transportation, search and rescue, and sightseeing [1]. With the deepening of these applications, AA Vs are required to operate in increasingly complex and dynamic flight environments, where moving obstacles, such as pedestrians, vehicles, and other AA Vs, present growing risks of collision [2].

-- T o address the challenges of localization drift and perception-planning coupling in unmanned aerial vehicles (UA Vs) operating in open-top scenarios (e.g., collapsed buildings, roofless mazes), this paper proposes EAROL, a novel framework with a downward-mounted tilted LiDAR configuration (20 inclination), integrating a LiDAR-Inertial Odometry (LIO) system and a hierarchical trajectory-yaw optimization algorithm. The hardware innovation enables constraint enhancement via dense ground point cloud acquisition and forward environmental awareness for dynamic obstacle detection. A tightly-coupled LIO system, empowered by an Iterative Error-State Kalman Filter (IESKF) with dynamic motion compensation, achieves high level 6-DoF localization accuracy in feature-sparse environments. Physical experiments demonstrate 81% tracking error reduction, 22% improvement in perceptual coverage, and near-zero vertical drift across indoor maze and 60-meter-scale outdoor scenarios. This work proposes a hardware-algorithm co-design paradigm, offering a robust solution for UA V autonomy in post-disaster search and rescue missions. I. INTRODUCTION Unmanned Aerial V ehicles (UA Vs) are currently widely used in various fields such as industry, agriculture, rescue operations, and photography [1]-[3].

We propose injecting notions of fairness into multi-robot motion planning. When robots have competing interests, it is important to optimize for some kind of fairness in their usage of resources. In this work, we explore how the robots' energy expenditures might be fairly distributed among them, while maintaining mission success. We formulate a distributed fair motion planner and integrate it with safe controllers in a algorithm called FiReFly. For simulated reach-avoid missions, FiReFly produces fairer trajectories and improves mission success rates over a non-fair planner. We find that real-time performance is achievable up to 15 UAVs, and that scaling up to 50 UAVs is possible with trade-offs between runtime and fairness improvements.

Urban Air Mobility (UAM) is an emerging transportation paradigm in which Uncrewed Aerial Systems (UAS) autonomously transport passengers and goods in cities. The UAS have different operators with different, sometimes competing goals, yet must share the airspace. We propose a negotiated, semi-distributed flight planner that optimizes UAS' flight lengths {\em in a fair manner}. Current flight planners might result in some UAS being given disproportionately shorter flight paths at the expense of others. We introduce Fair-CoPlan, a planner in which operators and a Provider of Service to the UAM (PSU) together compute \emph{fair} flight paths. Fair-CoPlan has three steps: First, the PSU constrains take-off and landing choices for flights based on capacity at and around vertiports. Then, operators plan independently under these constraints. Finally, the PSU resolves any conflicting paths, optimizing for path length fairness. By fairly spreading the cost of deconfliction Fair-CoPlan encourages wider participation in UAM, ensures safety of the airspace and the areas below it, and promotes greater operator flexibility. We demonstrate Fair-CoPlan through simulation experiments and find fairer outcomes than a non-fair planner with minor delays as a trade-off.

This study investigates the application of unmanned aerial vehicles (UAVs) in public management, focusing on optimizing path planning to address challenges such as energy consumption, obstacle avoidance, and airspace constraints. As UAVs transition from 'technical tools' to 'governance infrastructure', driven by advancements in low-altitude economy policies and smart city demands, efficient path planning becomes critical. The research proposes an enhanced Rapidly-exploring Random Tree algorithm (dRRT), incorporating four strategies: Target Bias (to accelerate convergence), Dynamic Step Size (to balance exploration and obstacle navigation), Detour Priority (to prioritize horizontal detours over vertical ascents), and B-spline smoothing (to enhance path smoothness). Simulations in a 500 m3 urban environment with randomized buildings demonstrate dRRT's superiority over traditional RRT, A*, and Ant Colony Optimization (ACO). Results show dRRT achieves a 100\% success rate with an average runtime of 0.01468s, shorter path lengths, fewer waypoints, and smoother trajectories (maximum yaw angles <45°). Despite improvements, limitations include increased computational overhead from added mechanisms and potential local optima due to goal biasing. The study highlights dRRT's potential for efficient UAV deployment in public management scenarios like emergency response and traffic monitoring, while underscoring the need for integration with real-time obstacle avoidance frameworks. This work contributes to interdisciplinary advancements in urban governance, robotics, and computational optimization.

During a U.S. naval test off the California coast last month, which was designed to showcase the Pentagon's top autonomous drone boats, one vessel stalled unexpectedly. As officials scrambled to fix a software glitch, another drone vessel smashed into the idling boat's starboard side, vaulted over the deck, and crashed back into the water -- an incident captured in videos. The previously unreported episode, which involved two vessels built by U.S. defense tech rivals Saronic and BlackSea Technologies, is one of a series of recent setbacks in the Pentagon's push to build a fleet of autonomous vessels, according to a dozen people familiar with the program.

Lt. Gen. Keith Kellogg discusses the latest with the Ukraine and Russia war after a deadly Russian attack on'America Reports.' A Russian drone may have crashed in a field in Poland, a move the country's deputy prime minister called a "provocation," as the United States and European leaders continue to push Moscow to end its war in Ukraine. The drone hit a cornfield in the village of Osiny in the eastern Lublin province, about 62 miles from Poland's border with Ukraine, Reuters reported. Deputy Prime Minister Wladyslaw Kosiniak-Kamysz, who also serves as defense minister, said Wednesday's incident was similar to cases in which Russian drones flew into Lithuania and Romania, and could be linked to efforts to end the war in Ukraine, according to the outlet. Polish police secure the area of a cornfield where an unidentified flying object has crashed and exploded in the country's east in Osiny on Wednesday.

NATO Secretary General Mark Rutte gives insight on the talks between President Donald Trump, Volodymyr Zelenskyy and European leaders, security guarantees for Ukraine and more on'The Ingraham Angle.' Two German warplanes were scrambled overnight from Romania after Russia launched a large-scale missile and drone attack in Ukraine less than a mile from the NATO borderline. Romania's Ministry of Defense said on Wednesday that two German Eurofighter Typhoon aircraft, stationed at Romania's Mihail Kogălniceanu Air Base as part of NATO's Enhanced Air Policing mission, were deployed "to monitor the air situation," but noted that this time no Russian aircraft or projectiles crossed the NATO border. Despite last week's talks between Russian President Vladimir Putin and President Donald Trump, Moscow has continued its aerial bombardment of Ukraine, including in an overnight attack that targeted oil and port facilities in the Odesa region on and near the Danube River, which separates the Ukrainian border with the allied NATO nation of Romania. The Eurofighter EF-2000 Typhoon of the German Air Force takes off from Los Llanos military air base during the Tactical Leadership Program in Albacete, Spain, on Nov. 21, 2024. The deployment of NATO jets comes after numerous incidents in recent weeks have increasingly threatened, and even crossed, NATO borders as the U.S. and Europe continue to push for Russia to end its war.