Defense Secretary Lloyd Austin on Wednesday said the U.S. will not stop flights in international airspace after a Russian fighter jet clipped a U.S. drone propeller, causing a crash into the Black Sea. Defense Secretary Lloyd Austin said on Wednesday the U.S. will not stop flights in international airspace after a Russian fighter jet clipped a U.S. drone propeller, causing a crash into the Black Sea. "I know that everyone here has heard that Russian aircraft again engaged in dangerous, reckless and unprofessional practices on Tuesday in international airspace over the Black Sea," Austin said. Defense Secretary Lloyd Austin speaks before a virtual meeting of the Ukraine Defense Contact Group on March 15, 2023, at the Pentagon in Washington, D.C. (ANDREW CABALLERO-REYNOLDS/POOL/AFP via Getty Images) Austin quickly summarized the incident, explaining, "Two Russian jets dumped fuel on an unmanned U.S. MQ-9 aircraft conducting routine operations in international airspace. And one Russian jet intercepted and hit our MQ-9 aircraft, resulting in a crash."

Defense Secretary Lloyd Austin on Wednesday said the U.S. will not stop flights in international airspace after a Russian fighter jet clipped a U.S. drone propeller, causing a crash into the Black Sea. A race between the U.S. and Russia is underway to secure the debris of the drone that crashed into the Black Sea. Russian officials announced Wednesday that operations were underway to collect the debris of the downed U.S. drone -- the country has denied responsibility for the incident. An MQ-9 Reaper remotely piloted aircraft (RPA) flies by during a training mission at Creech Air Force Base on Nov. 17, 2015, in Indian Springs, Nevada. U.S. officials say a Russian fighter jet clipped the U.S. drone's propeller while traveling in international airspace, causing the crash.

Republican Sen. Mike Rounds told Fox News' Jennifer Griffin Tuesday at a Ronald Reagan Presidential Foundation and Institute summit that he is angered by Russian fighter jet colliding with a U.S. drone over international waters. Republican Sen. Mike Rounds described himself as "p---ed off" following the reported collision of a Russian fighter jet with a U.S. military drone. U.S. military command alleged Tuesday that a Russian fighter jet dumped fuel on a U.S. drone over the Black Sea, clipped the drone's propeller and forced it into the water. Asked by Fox News' Jennifer Griffin for his feelings on the matter, Rounds. R-S.D., said he was "p---ed off."

Fox News correspondent Mike Tobin has the latest on the Russia-Ukraine war on'Special Report.' Tensions remain between Russia and the United States after a collision in international airspace. U.S. military command officials said Tuesday that a Russian fighter jet dumped fuel on a U.S. drone over the Black Sea, clipped the drone's propeller and forced it into the water. An MQ-9 Reaper remotely piloted aircraft is parked in a hanger at Creech Air Force Base in Indian Springs, Nevada. Russia is now denying that the aircraft touched one another, and accusing the U.S. of unnecessarily escalating the issue.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The U.K. and Germany scrambled fighter jets to intercept two Russian aircraft flying near Estonia late Tuesday. The Russian aircraft, a Russian Il-78 Midas refueling plane and an Antonov 148 military transport, approached NATO airspace without contacting Estonian authorities. The incident was the first time the U.K. and Germany have conducted a joint air intercept as part of the NATO treaty.

Washington's claim that a Russian fighter jet collided with a US surveillance drone near Crimea causing it to crash is both a rare military incident between the two superpowers and a serious escalation in already tense relations since Moscow's invasion of Ukraine last year. US and Russian officials have given conflicting accounts of what occurred on Tuesday over the Black Sea between the MQ-9 Reaper drone, valued at more than $30m and packed with sensitive US spying technology, and two Russian Su-27 fighter jets that were deployed to intercept the US aircraft. The Pentagon said two Russian Su-27 aircraft intercepted the drone and proceeded to dump fuel on the MQ-9 Reaper model as it conducted routine surveillance over the Black Sea in international airspace. US officials said the Russian jets flew around and in front of the drone several times for 30 to 40 minutes, and then one of the Su-27 fighters "struck the propeller" of the drone, "causing US forces to have to bring the MQ-9 down in international waters". A Pentagon spokesman said the collision likely damaged the Russian fighter jet, though the Su-27 did land.

This paper systematically studies the cooperative area coverage and target tracking problem of multiple-unmanned aerial vehicles (multi-UAVs). The problem is solved by decomposing into three sub-problems: information fusion, task assignment, and multi-UAV behavior decision-making. Specifically, in the information fusion process, we use the maximum consistency protocol to update the joint estimation states of multi-targets (JESMT) and the area detection information. The area detection information is represented by the equivalent visiting time map (EVTM), which is built based on the detection probability and the actual visiting time of the area. Then, we model the task assignment problem of multi-UAV searching and tracking multi-targets as a network flow model with upper and lower flow bounds. An algorithm named task assignment minimum-cost maximum-flow (TAMM) is proposed. Cooperative behavior decision-making uses Fisher information as the mission reward to obtain the optimal tracking action of the UAV. Furthermore, a coverage behavior decision-making algorithm based on the anti-flocking method is designed for those UAVs assigned the coverage task. Finally, a distributed multi-UAV cooperative area coverage and target tracking algorithm is designed, which integrates information fusion, task assignment, and behavioral decision-making. Numerical and hardware-in-the-loop simulation results show that the proposed method can achieve persistent area coverage and cooperative target tracking.

For the purpose of inspecting power plants, autonomous robots can be built using reinforcement learning techniques. The method replicates the environment and employs a simple reinforcement learning (RL) algorithm. This strategy might be applied in several sectors, including the electricity generation sector. A pre-trained model with perception, planning, and action is suggested by the research. To address optimization problems, such as the Unmanned Aerial Vehicle (UAV) navigation problem, Deep Q-network (DQN), a reinforcement learning-based framework that Deepmind launched in 2015, incorporates both deep learning and Q-learning. To overcome problems with current procedures, the research proposes a power plant inspection system incorporating UAV autonomous navigation and DQN reinforcement learning. These training processes set reward functions with reference to states and consider both internal and external effect factors, which distinguishes them from other reinforcement learning training techniques now in use. The key components of the reinforcement learning segment of the technique, for instance, introduce states such as the simulation of a wind field, the battery charge level of an unmanned aerial vehicle, the height the UAV reached, etc. The trained model makes it more likely that the inspection strategy will be applied in practice by enabling the UAV to move around on its own in difficult environments. The average score of the model converges to 9,000. The trained model allowed the UAV to make the fewest number of rotations necessary to go to the target point.

The adoption process of innovative software-intensive technologies leverages complex trust concerns in different forms and shapes. Perceived safety plays a fundamental role in technology adoption, being especially crucial in the case of those innovative software-driven technologies characterized by a high degree of dynamism and unpredictability, like collaborating autonomous systems. These systems need to synchronize their maneuvers in order to collaboratively engage in reactions to unpredictable incoming hazardous situations. That is however only possible in the presence of mutual trust. In this paper, we propose an approach for machine-to-machine dynamic trust assessment for collaborating autonomous systems that supports trust-building based on the concept of dynamic safety assurance within the collaborative process among the software-intensive autonomous systems. In our approach, we leverage the concept of digital twins which are abstract models fed with real-time data used in the run-time dynamic exchange of information. The information exchange is performed through the execution of specialized models that embed the necessary safety properties. More particularly, we examine the possible role of the Digital Twins in machine-to-machine trust building and present their design in supporting dynamic trust assessment of autonomous drones. Ultimately, we present a proof of concept of direct and indirect trust assessment by employing the Digital Twin in a use case involving two autonomous collaborating drones.

Unmanned aerial vehicles (UAVs) are seen as a promising technology to perform a wide range of tasks in wireless communication networks. In this work, we consider the deployment of a group of UAVs to collect the data generated by IoT devices. Specifically, we focus on the case where the collected data is time-sensitive, and it is critical to maintain its timeliness. Our objective is to optimally design the UAVs' trajectories and the subsets of visited IoT devices such as the global Age-of-Updates (AoU) is minimized. To this end, we formulate the studied problem as a mixed-integer nonlinear programming (MINLP) under time and quality of service constraints. To efficiently solve the resulting optimization problem, we investigate the cooperative Multi-Agent Reinforcement Learning (MARL) framework and propose an RL approach based on the popular on-policy Reinforcement Learning (RL) algorithm: Policy Proximal Optimization (PPO). Our approach leverages the centralized training decentralized execution (CTDE) framework where the UAVs learn their optimal policies while training a centralized value function. Our simulation results show that the proposed MAPPO approach reduces the global AoU by at least a factor of 1/2 compared to conventional off-policy reinforcement learning approaches.