This paper presents a neural network that effectively removes visual defects from UAV-captured images. It features an enhanced Pix2Pix GAN, specifically engineered to address visual defects in UAV imagery. The method incorporates advanced modifications to the Pix2Pix architecture, targeting prevalent issues such as mode collapse. The suggested method facilitates significant improvements in the quality of defected UAV images, yielding cleaner and more precise visual results. The effectiveness of the proposed approach is demonstrated through evaluation on a custom dataset of aerial photographs, highlighting its capability to refine and restore UAV imagery effectively.

This letter presents a novel multi-robot task allocation and path planning method that considers robots' maximum range constraints in large-sized workspaces, enabling robots to complete the assigned tasks within their range limits. Firstly, we developed a fast path planner to solve global paths efficiently. Subsequently, we propose an innovative auction-based approach that integrates our path planner into the auction phase for reward computation while considering the robots' range limits. This method accounts for extra obstacle-avoiding travel distances rather than ideal straight-line distances, resolving the coupling between task allocation and path planning. Additionally, to avoid redundant computations during iterations, we implemented a lazy auction strategy to speed up the convergence of the task allocation. Finally, we validated the proposed method's effectiveness and application potential through extensive simulation and real-world experiments. The implementation code for our method will be available at https://github.com/wuuya1/RangeTAP.

Latvia and Romania, two member countries of the North Atlantic Treaty Organization (NATO), said Russian drones violated their airspace over the weekend in a move that could stoke boiling-hot tensions between Moscow and the military alliance. Latvia's government said Sunday a Russian drone had fallen over the east of the country the previous day, likely crossing in from Belarus. Separately, on Sunday, Romania's foreign ministry said "criminal" Russian airborne drones encroached on its airspace while targeting Ukraine's civilian infrastructure. Mircea Geoana, the outgoing deputy secretary general of NATO and Romania's former top diplomat, said the military alliance condemned Russia's violation of Romanian airspace. "While we have no information indicating an intentional attack by Russia against allies, these acts are irresponsible and potentially dangerous," he wrote on X, formerly Twitter.

Ukraine is adding little-known incendiary weapons to its armoury in its battle to fend off the ongoing Russian invasion, including "fire-spitting" drones reminiscent of dragons. On Wednesday, Ukraine's Defence Ministry posted videos on the social media platform X showing a Ukrainian drone raining down what appeared to be fire – but was molten metal – on forested positions presumed to be hiding Russian units. "A'dragon drone' in the direction of Kharkiv", the post from the ministry read, referring to Ukraine's second-largest city, which has been the target of repeated Russian bombing. Analysts say the weapon is a new and innovative introduction of an age-old weapon into the strategy of a Ukrainian military that has shown its growing proficiency in using small drones. Here's what to know about the new "dragon drones": Dragon drones carry a substance called thermite.

The gut-wrenching loss of an Ukraine Air Force F-16 chasing Iranian-made drones at low altitude last week is proof positive that Ukraine's air force is becoming more aggressive and capable. The daily air battles against missiles and drones over Ukraine are only a taste of what U.S. bases and allies could experience in the event of Chinese attacks. This I can tell you. Almost certainly, the pilot, identified as Col. Alexei "Moonfish" Mes, was being very aggressive in defense of his homeland. Russia had launched an attack on Ukraine with 127 missiles and 109 one-way attack drones on Aug. 26.

Latvia and Romania, NATO members who are allies of Ukraine, have said that Russian drones violated their airspace. Romania said a Russian drone entered its airspace during nighttime attacks across the Danube River in neighbouring Ukraine in the early hours of Sunday, while Latvia said one crashed in the eastern part of the country a day earlier. Romania's Ministry of National Defence said that Bucharest deployed F-16 fighter jets to monitor its airspace and that a search for the weapon's debris was under way at a potential crash site near the border. There were no immediate reports of casualties or damage. Bucharest strongly condemned the "renewed violation" brought on by Moscow's "illegal attacks".

Perception-based navigation systems are useful for unmanned ground vehicle (UGV) navigation in complex terrains, where traditional depth-based navigation schemes are insufficient. However, these data-driven methods are highly dependent on their training data and can fail in surprising and dramatic ways with little warning. To ensure the safety of the vehicle and the surrounding environment, it is imperative that the navigation system is able to recognize the predictive uncertainty of the perception model and respond safely and effectively in the face of uncertainty. In an effort to enable safe navigation under perception uncertainty, we develop a probabilistic and reconstruction-based competency estimation (PaRCE) method to estimate the model's level of familiarity with an input image as a whole and with specific regions in the image. We find that the overall competency score can correctly predict correctly classified, misclassified, and out-of-distribution (OOD) samples. We also confirm that the regional competency maps can accurately distinguish between familiar and unfamiliar regions across images. We then use this competency information to develop a planning and control scheme that enables effective navigation while maintaining a low probability of error. We find that the competency-aware scheme greatly reduces the number of collisions with unfamiliar obstacles, compared to a baseline controller with no competency awareness. Furthermore, the regional competency information is very valuable in enabling efficient navigation.

The Orienteering Problem (OP) is a well-studied routing problem that has been extended to incorporate uncertainties, reflecting stochastic or dynamic travel costs, prize-collection costs, and prizes. Existing approaches may, however, be inefficient in real-world applications due to insufficient modeling knowledge and initially unknowable parameters in online scenarios. Thus, we propose the Uncertain and Dynamic Orienteering Problem (UDOP), modeling travel costs as distributions with unknown and time-variant parameters. UDOP also associates uncertain travel costs with dynamic prizes and prize-collection costs for its objective and budget constraints. To address UDOP, we develop an ADaptive Approach for Probabilistic paThs - ADAPT, that iteratively performs 'execution' and 'online planning' based on an initial 'offline' solution. The execution phase updates system status and records online cost observations. The online planner employs a Bayesian approach to adaptively estimate power consumption and optimize path sequence based on safety beliefs. We evaluate ADAPT in a practical Unmanned Aerial Vehicle (UAV) charging scheduling problem for Wireless Rechargeable Sensor Networks. The UAV must optimize its path to recharge sensor nodes efficiently while managing its energy under uncertain conditions. ADAPT maintains comparable solution quality and computation time while offering superior robustness. Extensive simulations show that ADAPT achieves a 100% Mission Success Rate (MSR) across all tested scenarios, outperforming comparable heuristic-based and frequentist approaches that fail up to 70% (under challenging conditions) and averaging 67% MSR, respectively. This work advances the field of OP with uncertainties, offering a reliable and efficient approach for real-world applications in uncertain and dynamic environments.

Despite the growing impact of Unmanned Aerial Vehicles (UAVs) across various industries, most of current available solutions lack for a robust autonomous navigation system to deal with the appearance of obstacles safely. This work presents an approach to perform autonomous UAV planning and navigation in scenarios in which a safe and high maneuverability is required, due to the cluttered environment and the narrow rooms to move. The system combines an RRT* global planner with a newly proposed reactive planner, DWA-3D, which is the extension of the well known DWA method for 2D robots. We provide a theoretical-empirical method for adjusting the parameters of the objective function to optimize, easing the classical difficulty for tuning them. An onboard LiDAR provides a 3D point cloud, which is projected on an Octomap in which the planning and navigation decisions are made. There is not a prior map; the system builds and updates the map online, from the current and the past LiDAR information included in the Octomap. Extensive real-world experiments were conducted to validate the system and to obtain a fine tuning of the involved parameters. These experiments allowed us to provide a set of values that ensure safe operation across all the tested scenarios. Just by weighting two parameters, it is possible to prioritize either horizontal path alignment or vertical (height) tracking, resulting in enhancing vertical or lateral avoidance, respectively. Additionally, our DWA-3D proposal is able to navigate successfully even in absence of a global planner or with one that does not consider the drone's size. Finally, the conducted experiments show that computation time with the proposed parameters is not only bounded but also remains stable around 40 ms, regardless of the scenario complexity.

The Houthis have claimed to have shot down a United States military drone over Yemen, in the latest attack by the group, which has disrupted shipping trade through the crucial Bab al-Mandeb Strait, drawing US strikes. The Yemeni group has carried out dozens of attacks on ships with links to Israel in a show of solidarity with Palestinians amid Israel's 11-month-old war on Gaza. Yahya Saree, the military spokesman of the Houthi group, said in a prerecorded video message released early on Sunday that the MQ-9 Reaper was shot down by air defences over Marib as "it was carrying out hostile activities". This is the eighth drone of this type to be shot down since the start of the war on Gaza, he said. The group has not so far released footage of the downed attack and surveillance aircraft that costs about 30m.