It has become conventional wisdom among the halls of the United States government that China will launch a full-scale invasion of Taiwan within the next few years. And when that happens, the US military has a relatively straightforward response in mind: Unleash hell. Speaking to The Washington Post on the sidelines of the International Institute for Strategic Studies' annual Shangri-La Dialogue in June, US Indo-Pacific Command chief Navy Admiral Samuel Paparo colorfully described the US military's contingency plan for a Chinese invasion of Taiwan as flooding the narrow Taiwan Strait between the two countries with swarms of thousands upon thousands of drones, by land, sea, and air, to delay a Chinese attack enough for the US and its allies to muster additional military assets in the region. "I want to turn the Taiwan Strait into an unmanned hellscape using a number of classified capabilities," Paparo said, "so that I can make their lives utterly miserable for a month, which buys me the time for the rest of everything." Cheap, easily weaponizable drones have transformed battlefields from Ukraine to the Middle East in recent years, and the US military is rapidly adapting to this new uncrewed future.

Ukrainian President Volodymyr Zelenskyy for the first time stated the aim of Ukraine's August 6 incursion into Russia's Kursk region, saying the operation was necessary to create a buffer zone. Ukrainian Air Force commander Mykola Oleshchuk said the air force destroyed a second strategically important bridge over the Seym River in the Kursk region. He posted an aerial video of a blast tearing through the bridge, which appeared to be near the village of Zvannoye, about 15km (nine miles) north of the Ukrainian border. Vasily Golubev, the governor of Russia's southern Rostov region, said falling debris from a Ukrainian drone attack triggered a large fire at an oil storage facility in the town of Proletarsk. There were no reports of injuries.

This paper introduces a system designed for tight collaboration between Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs) in harsh maritime conditions characterized by large waves. This onboard UAV system aims to enhance collaboration with USVs for following and landing tasks under such challenging conditions. The main contribution of our system is the novel mathematical USV model, describing the movement of the USV in 6 degrees of freedom on a wavy water surface, which is used to estimate and predict USV states. The estimator fuses data from multiple global and onboard sensors, ensuring accurate USV state estimation. The predictor computes future USV states using the novel mathematical USV model and the last estimated states. The estimated and predicted USV states are forwarded into a trajectory planner that generates a UAV trajectory for following the USV or landing on its deck, even in harsh environmental conditions. The proposed approach was verified in numerous simulations and deployed to the real world, where the UAV was able to follow the USV and land on its deck repeatedly.

Belarusian President Alexander Lukashenko says Kyiv has stationed more than 120,000 soldiers along its border with Belarus, the country's state news agency reported, as fighting continues amid Ukraine's incursion into Russia's Kursk region. Lukashenko, a staunch ally of Russian President Vladimir Putin, said on Sunday that Minsk had deployed nearly a third of its armed forces along the entire border in response to the Ukrainian deployment, BelTA reported. Kyiv did not immediately respond to the claims. "Seeing their aggressive policy, we have introduced there and placed in certain points – in case of war, they would be defence – our military along the entire border," BelTA quoted Lukashenko as saying in an interview with Russian state television. The president made it clear that should Ukraine try to enter Belarusian soil, they will be on the offensive, Jabari added.

Unmanned Aerial Vehicles (UAVs), due to their low cost and high flexibility, have been widely used in various scenarios to enhance network performance. However, the optimization of UAV trajectories in unknown areas or areas without sufficient prior information, still faces challenges related to poor planning performance and low distributed execution. These challenges arise when UAVs rely solely on their own observation information and the information from other UAVs within their communicable range, without access to global information. To address these challenges, this paper proposes the Qedgix framework, which combines graph neural networks (GNNs) and the QMIX algorithm to achieve distributed optimization of the Age of Information (AoI) for users in unknown scenarios. The framework utilizes GNNs to extract information from UAVs, users within the observable range, and other UAVs within the communicable range, thereby enabling effective UAV trajectory planning. Due to the discretization and temporal features of AoI indicators, the Qedgix framework employs QMIX to optimize distributed partially observable Markov decision processes (Dec-POMDP) based on centralized training and distributed execution (CTDE) with respect to mean AoI values of users. By modeling the UAV network optimization problem in terms of AoI and applying the Kolmogorov-Arnold representation theorem, the Qedgix framework achieves efficient neural network training through parameter sharing based on permutation invariance. Simulation results demonstrate that the proposed algorithm significantly improves convergence speed while reducing the mean AoI values of users. The code is available at https://github.com/UNIC-Lab/Qedgix.

Harnessing human movements to command an Unmanned Aerial Vehicle (UAV) holds the potential to revolutionize their deployment, rendering it more intuitive and user-centric. In this research, we introduce a novel methodology adept at classifying three-dimensional human actions, leveraging them to coordinate on-field with a UAV. Utilizing a stereo camera, we derive both RGB and depth data, subsequently extracting three-dimensional human poses from the continuous video feed. This data is then processed through our proposed k-nearest neighbour classifier, the results of which dictate the behaviour of the UAV. It also includes mechanisms ensuring the robot perpetually maintains the human within its visual purview, adeptly tracking user movements. We subjected our approach to rigorous testing involving multiple tests with real robots. The ensuing results, coupled with comprehensive analysis, underscore the efficacy and inherent advantages of our proposed methodology.

We present a comprehensive review of the design landscape for resilient autonomous machines. We show that existing techniques are of a "one-size-fits-all" nature, where the same protection scheme is applied to the entire software stack, leading to either high overhead or low protection strength. We provide a thorough characterization of the inherent resilience of different tasks in widely used, open source software stacks for autonomous vehicles (AutoWare) and drones (MAVBench). We show that different tasks vary significantly in their resilience under hardware faults. In particular, front-end machine vision tasks that operate on massive visual data are much more resilient to faults than back-end tasks, such as planning and control, which operate on smaller data but are more sensitive to faults. We propose VAP for resilient autonomous machines. In VAP, we spend less protection efforts on front-end machine-vision tasks and more budget on back-end planning and control tasks. Experimentally, we show that the VAP mechanism provides high protection coverage while maintaining low protection overhead on both autonomous vehicle and drone systems.

Drones have been a mainstay technology among militaries, hobbyists, and first responders alike for more than a decade. No longer limited to small quadcopters with insufficient battery life, drones are aiding search and rescue efforts, reshaping wars in Ukraine and Gaza, and delivering time-sensitive packages of medical supplies. And billions of dollars are being plowed into building the next generation of fully autonomous systems. These developments raise a number of questions: Are drones safe enough to be flown in dense neighborhoods and cities? Is it a violation of people's privacy for police to fly drones overhead at an event or protest?

These developments raise a number of questions: Are drones safe enough to be flown in dense neighborhoods and cities? Is it a violation of people's privacy for police to fly drones overhead at an event or protest? Who decides what level of drone autonomy is acceptable in a war zone? Those questions are no longer hypothetical. Advancements in drone technology and sensors, falling prices, and easing regulations are making drones cheaper, faster, and more capable than ever.

Robotic fruit harvesting holds potential in precision agriculture to improve harvesting efficiency. While ground mobile robots are mostly employed in fruit harvesting, certain crops, like avocado trees, cannot be harvested efficiently from the ground alone. This is because of unstructured ground and planting arrangement and high-to-reach fruits. In such cases, aerial robots integrated with manipulation capabilities can pave new ways in robotic harvesting. This paper outlines the design and implementation of a bimanual UAV that employs visual perception and learning to autonomously detect avocados, reach, and harvest them. The dual-arm system comprises a gripper and a fixer arm, to address a key challenge when harvesting avocados: once grasped, a rotational motion is the most efficient way to detach the avocado from the peduncle; however, the peduncle may store elastic energy preventing the avocado from being harvested. The fixer arm aims to stabilize the peduncle, allowing the gripper arm to harvest. The integrated visual perception process enables the detection of avocados and the determination of their pose; the latter is then used to determine target points for a bimanual manipulation planner. Several experiments are conducted to assess the efficacy of each component, and integrated experiments assess the effectiveness of the system.