The leaders of the United States and Australia agreed Wednesday to expand defense cooperation with Japan to include unmanned aerial vehicles as Washington continues to bolster relations with its Asia-Pacific allies and partners to maintain its edge in the face of China's growing military might. Following a meeting at the White House, U.S. President Joe Biden and Australian Prime Minister Anthony Albanese said the three-way partnership aims to enhance interoperability and accelerate technology transfer in the rapidly emerging field of "collaborative combat aircraft and autonomy," -- a U.S. Air Force concept referring to autonomous drone operations and manned-unmanned teaming. No further details were provided, but the announcement comes after the Pentagon unveiled its "Replicator" initiative last month: a radical new strategy focused on fielding thousands of cheap autonomous drones within 18 to 24 months to counter China's military advantage in personnel and manned equipment.

Russian Defence Minister Sergei Shoigu has visited a command post near the front lines in eastern Ukraine as fighting in the region intensifies in advance of the harsh winter season. He travelled to the "Vostok" command post in the east to be briefed on developments at the front as Russian forces stepped up attacks, according to footage posted by the Ministry of Defence on Wednesday. The minister was briefed on preparations for combat for the forthcoming winter and the training of drone operators, the Ministry of Defence said, according to AFP. "The situation today suggests the enemy has fewer and fewer opportunities. And they will continue to be reduced, thanks exclusively to your combat work," Shoigu told Russian soldiers as he sought to raise morale. The Ministry of Defence, whose video showed Shoigu arriving at the post via helicopter, added that the minister "drew special attention to the timely and sufficient provision of new winter uniforms and insulated footwear for all personnel" before winter, when temperatures plunge below freezing.

Russian Defence Minister Sergei Shoigou has visited a command post near the front lines in eastern Ukraine as fighting in the region intensifies in advance of the harsh winter season. He travelled to the "Vostok" command post in the east to be briefed on developments at the front as Russian forces stepped up attacks, according to footage posted by the Ministry of Defence on Wednesday. The minister was briefed on preparations for combat for the forthcoming winter and the training of drone operators, the Ministry of Defence said, according to AFP. "The situation today suggests the enemy has fewer and fewer opportunities. And they will continue to be reduced, thanks exclusively to your combat work," Shoigou told Russian soldiers as he sought to raise morale. The Ministry of Defence, whose video showed Shoigou arriving at the post via helicopter, added that the minister "drew special attention to the timely and sufficient provision of new winter uniforms and insulated footwear for all personnel" before winter, when temperatures plunge below freezing.

In recent years, the combination of artificial intelligence (AI) and unmanned aerial vehicles (UAVs) has brought about advancements in various areas. This comprehensive analysis explores the changing landscape of AI-powered UAVs and friendly computing in their applications. It covers emerging trends, futuristic visions, and the inherent challenges that come with this relationship. The study examines how AI plays a role in enabling navigation, detecting and tracking objects, monitoring wildlife, enhancing precision agriculture, facilitating rescue operations, conducting surveillance activities, and establishing communication among UAVs using environmentally conscious computing techniques. By delving into the interaction between AI and UAVs, this analysis highlights the potential for these technologies to revolutionise industries such as agriculture, surveillance practices, disaster management strategies, and more. While envisioning possibilities, it also takes a look at ethical considerations, safety concerns, regulatory frameworks to be established, and the responsible deployment of AI-enhanced UAV systems. By consolidating insights from research endeavours in this field, this review provides an understanding of the evolving landscape of AI-powered UAVs while setting the stage for further exploration in this transformative domain.

Kurt "The CyberGuy" Knutsson introduces Somatic's AI janitor robot that was created to help with cleaning restrooms. Oregon State University is warning students to "avoid all robots" and to "not open" any food delivery robots due to an ongoing bomb threat on the campus. On Tuesday afternoon, Oregon State University (OSU) issued an alert to students at the Corvallis, Oregon, university that there was a bomb threat related to the Starship food delivery robots. Oregon State University told students to avoid Starship food delivery robots due to a bomb threat. OSU advised people not open the robots and to avoid them "until further notice."

In recent years, the need for resources for handling processes with high computational complexity for mobile robots is becoming increasingly urgent. More specifically, robots need to autonomously operate in a robust and continuous manner, while keeping high performance, a need that led to the utilization of edge computing to offload many computationally demanding and time-critical robotic procedures. However, safe mechanisms should be implemented to handle situations when it is not possible to use the offloaded procedures, such as if the communication is challenged or the edge cluster is not available. To this end, this article presents a switching strategy for safety, redundancy, and optimized behavior through an edge computing-based Model Predictive Controller (MPC) and a low-level onboard-PID controller for edge-connected Unmanned Aerial Vehicles (UAVs). The switching strategy is based on the communication Key Performance Indicators (KPIs) over 5G to decide whether the UAV should be controlled by the edge-based or have a safe fallback based on the onboard controller.

Deep Reinforcement Learning (DRL) is widely used to optimize the performance of multi-UAV networks. However, the training of DRL relies on the frequent interactions between the UAVs and the environment, which consumes lots of energy due to the flying and communication of UAVs in practical experiments. Inspired by the growing digital twin (DT) technology, which can simulate the performance of algorithms in the digital space constructed by coping features of the physical space, the DT is introduced to reduce the costs of practical training, e.g., energy and hardware purchases. Different from previous DT-assisted works with an assumption of perfect reflecting real physics by virtual digital, we consider an imperfect DT model with deviations for assisting the training of multi-UAV networks. Remarkably, to trade off the training cost, DT construction cost, and the impact of deviations of DT on training, the natural and virtually generated UAV mixing deployment method is proposed. Two cascade neural networks (NN) are used to optimize the joint number of virtually generated UAVs, the DT construction cost, and the performance of multi-UAV networks. These two NNs are trained by unsupervised and reinforcement learning, both low-cost label-free training methods. Simulation results show the training cost can significantly decrease while guaranteeing the training performance. This implies that an efficient decision can be made with imperfect DTs in multi-UAV networks.

Iran-backed militias in Iraq have claimed they were responsible for an attack on U.S. forces at a strategic base in southeastern Syria. The Islamic Resistance in Iraq, an umbrella group of Iranian-backed militias, said Monday that their forces used two drones to attack the al-Tanf garrison near the Jordanian and Iraqi borders, a sensitive location often used by Iranian-backed militants to transport weapons to Hezbollah. Monday's attack comes after a string of similar attacks on bases housing U.S. military in Iraq and Syria over the past week. In one, the same group attacked two bases in Iraq with drones, causing minor injuries among U.S. forces. The U.S. military has maintained a presence at the al-Tanf garrison since training forces as part of a campaign against the Islamic State group.

FOX News contributor Dr. Rebecca Grant tells'FOX News Live' that she believes tensions in the Middle East can be contained to just Israel. The State Department on Sunday updated its travel advisory for Iraq to include the ordered departure of all non-emergency U.S. government personnel and eligible family members. Americans are warned "do not travel to Iraq due to terrorism, kidnapping, armed conflict, civil unrest, and Mission Iraq's limited capacity to provide support to U.S. citizens." On Oct. 20, the State Department already ordered the departure of eligible family members and non-emergency U.S. government personnel from U.S. Embassy Baghdad and U.S. Consulate General Erbil "due to increased security threats against U.S. government personnel and interests." In recent days, Iran-backed militias attacked United States military bases in Iraq.

The field of safe multi-agent reinforcement learning, despite its potential applications in various domains such as drone delivery and vehicle automation, remains relatively unexplored. Training agents to learn optimal policies that maximize rewards while considering specific constraints can be challenging, particularly in scenarios where having a central controller to coordinate the agents during the training process is not feasible. In this paper, we address the problem of multi-agent policy optimization in a decentralized setting, where agents communicate with their neighbors to maximize the sum of their cumulative rewards while also satisfying each agent's safety constraints. We consider both peak and average constraints. In this scenario, there is no central controller coordinating the agents and both the rewards and constraints are only known to each agent locally/privately. We formulate the problem as a decentralized constrained multi-agent Markov Decision Problem and propose a momentum-based decentralized policy gradient method, DePAint, to solve it. To the best of our knowledge, this is the first privacy-preserving fully decentralized multi-agent reinforcement learning algorithm that considers both peak and average constraints. We also provide theoretical analysis and empirical evaluation of our algorithm in various scenarios and compare its performance to centralized algorithms that consider similar constraints.