Cooperation between the U.S. and its allies and partners, particularly those in the Indo-Pacific, will be critical for Washington's new Replicator initiative to succeed, experts say, as the Pentagon seeks to negate China's military advantage in numbers by fielding thousands of smart, affordable drones. But questions remain about how much technology Washington will be willing to share without the risk of compromising the high degree of cybersecurity the new artificial intelligence-enabled systems will depend on. "We will be working with industry, Congress and allies and partners in everything that we do," U.S. Deputy Secretary of Defense Kathleen Hicks, who is heading up the initiative, said Sept. 6, as experts highlighted an array of possible collaboration opportunities.

Autonomous inspection tasks necessitate effective path-planning mechanisms to efficiently gather observations from points of interest (POI). However, localization errors commonly encountered in urban environments can introduce execution uncertainty, posing challenges to the successful completion of such tasks. To tackle these challenges, we present IRIS-under uncertainty (IRIS-U^2), an extension of the incremental random inspection-roadmap search (IRIS) algorithm, that addresses the offline planning problem via an A*-based approach, where the planning process occurs prior the online execution. The key insight behind IRIS-U^2 is transforming the computed localization uncertainty, obtained through Monte Carlo (MC) sampling, into a POI probability. IRIS-U^2 offers insights into the expected performance of the execution task by providing confidence intervals (CI) for the expected coverage, expected path length, and collision probability, which becomes progressively tighter as the number of MC samples increase. The efficacy of IRIS-U^2 is demonstrated through a case study focusing on structural inspections of bridges. Our approach exhibits improved expected coverage, reduced collision probability, and yields increasingly-precise CIs as the number of MC samples grows. Furthermore, we emphasize the potential advantages of computing bounded sub-optimal solutions to reduce computation time while still maintaining the same CI boundaries.

In different wireless network scenarios, multiple network entities need to cooperate in order to achieve a common task with minimum delay and energy consumption. Future wireless networks mandate exchanging high dimensional data in dynamic and uncertain environments, therefore implementing communication control tasks becomes challenging and highly complex. Multi-agent reinforcement learning with emergent communication (EC-MARL) is a promising solution to address high dimensional continuous control problems with partially observable states in a cooperative fashion where agents build an emergent communication protocol to solve complex tasks. This paper articulates the importance of EC-MARL within the context of future 6G wireless networks, which imbues autonomous decision-making capabilities into network entities to solve complex tasks such as autonomous driving, robot navigation, flying base stations network planning, and smart city applications. An overview of EC-MARL algorithms and their design criteria are provided while presenting use cases and research opportunities on this emerging topic.

Now it's Russia that appears to have most to worry about, as Ukrainian drones and commandos launch raids on the northwest corner of Crimea, damaging a radar base on the Tarkhankut Peninsula and even planting a Ukrainian flag during an operation to mark Independence Day, on 24 August.

Drones are increasingly operating autonomously, and the need for extending drone power autonomy is rapidly increasing. One of the most promising solutions to extend drone power autonomy is the use of docking stations to support both landing and recharging of the drone. To this end, we introduce a novel wireless drone docking station with three commercial wireless charging modules. We have developed two independent units, both in mechanical and electrical aspects: the energy transmitting unit and the energy receiving unit. We have also studied the efficiency of wireless power transfer and demonstrated the advantages of connecting three receiver modules connected in series and parallel. We have achieved maximum output power of 96.5 W with a power transfer efficiency of 56.6% for the series connection of coils. Finally, we implemented the system in practice on a drone and tested both energy transfer and landing.

In recent years, the use of inspection drones has become increasingly popular for high-voltage electric cable inspections due to their efficiency, cost-effectiveness, and ability to access hard-to-reach areas. However, safely landing drones on power lines, especially under windy conditions, remains a significant challenge. This study introduces a semi-autonomous control scheme for landing on an electrical line with the NADILE drone (an experimental drone based on original LineDrone key features for inspection of power lines) and assesses the operating envelope under various wind conditions. A Monte Carlo method is employed to analyze the success probability of landing given initial drone states. The performance of the system is evaluated for two landing strategies, variously controllers parameters and four level of wind intensities. The results show that a two-stage landing strategies offers higher probabilities of landing success and give insight regarding the best controller parameters and the maximum wind level for which the system is robust. Lastly, an experimental demonstration of the system landing autonomously on a power line is presented.

In the last decade, UAVs have become a widely used technology. As they are used by both professionals and amateurs, there is a need to explore different control modalities to make control intuitive and easier, especially for new users. In this work, we compared the most widely used joystick control with a custom human pose control. We used human pose estimation and arm movements to send UAV commands in the same way that operators use their fingers to send joystick commands. Experiments were conducted in a simulation environment with first-person visual feedback. Participants had to traverse the same maze with joystick and human pose control. Participants' subjective experience was assessed using the raw NASA Task Load Index.

Secretary of State Antony Blinken twice declined to criticize Elon Musk after the SpaceX founder said he refused to help the Ukrainian government access his Starlink internet service in order to attack Russia. Blinken was pressed by CNN's Jake Tapper to comment on details in a new book confirmed by Musk, including that he refused the Ukrainian government's requests to activate Starlink, a satellite internet service run by SpaceX, in Crimea so it could launch a submarine drone attack against Russian naval forces. "There was an emergency request from government authorities to activate Starlink all the way to Sevastopol," Musk posted Thursday on X. "The obvious intent being to sink most of the Russian fleet at anchor. If I had agreed to their request, then SpaceX would be explicitly complicit in a major act of war and conflict escalation." Tapper asked Blinken whether Musk should face repercussions after he "effectively sabotaged a military operation by Ukraine, a U.S. ally, against Russia, an aggressor country that invaded a U.S. ally."

A drone attack on an open market south of the Sudanese capital, Khartoum, has killed at least 40 people, activists and medical workers said, as the military and the paramilitary Rapid Support Forces (RSF) battle for control of the country. At least 70 others were injured in the attack in Khartoum's Mayo neighbourhood on Sunday, according to resistance committees and two healthcare workers at the Bashair University Hospital, where the casualties were treated. Many of them will require amputations. The group posted footage on social media showing bodies wrapped in white sheets in an open yard at the hospital. Reporting from Khartoum, Al Jazeera's Hiba Morgan said the drone attack was carried out by the Sudanese army.

Ukraine has reported downing more than two dozen Russian drones over the country's capital, Kyiv, as Russia's defence ministry announced the destruction of eight Ukrainian drones near the annexed Crimean peninsula. The extent of the damage from the rival attacks early on Sunday was not immediately clear. Kyiv Mayor Vitali Klitschko said that at least one person was wounded in the city's historic Podil neighbourhood and a fire broke out near one of its parks. Debris from downed drones fell on the Darnytskyi, Solomianskyi, Shevchenkivskyi, Sviatoshynskyi and Podil districts, Klitschko and the city's military administration said. In the Shevchenkivskyi district, debris sparked a fire in an apartment, which was quickly distinguished.