A Russian soldier was seen surrendering to a Ukrainian drone May 9 in edited video released by Ukraine's 92nd Mechanized Brigade. A Russian soldier whose surrender to Ukrainian forces was captured on drone camera, spoke for the first time about his experience. Ruslan Anitin, a draftee who was cornered alone by the Ukrainian military near the city of Bakhmut, surrendered by communicating via an aerial drone's camera. "It felt like it was never going to involve us at all," Anitin said of the conflict during an interview with the Wall Street Journal about his experience. A Russian soldier was seen surrendering to a Ukrainian drone May 9 in edited video released by Ukraine's 92nd Mechanized Brigade.

Kyiv, Ukraine – Standing next to the coffin with the body of Daniil Maznik, many of his camouflage-clad, battle-tested brothers-in-arms wept. "He was a brave warrior, a pious Christian, a trustworthy comrade," Denis Kapustin, Maznik's commanding officer, said through tears during a farewell ceremony last weekend at the historic Baikove cemetery in Kyiv. Maznik, a bearded and burly 29-year-old, was killed during one of the most audacious and brazen military operations of the continuing Russia-Ukraine war. On June 1, he was part of four small military units that crossed into the western Russian region of Belgorod to attack Shebekino, a city of 40,000, and seize the village of Novaya Tavolzhanka. They clashed with border guards and servicemen and were backed by Ukrainian drone attacks and heavy, indiscriminate artillery fire that included banned cluster munitions, Russian officials claimed.

From TikTok to Huawei routers to DJI drones, rising tensions between China and the US have made Americans--and the US government--increasingly wary of Chinese-owned technologies. But thanks to the complexity of the hardware supply chain, encryption chips sold by the subsidiary of a company specifically flagged in warnings from the US Department of Commerce for its ties to the Chinese military have found their way into the storage hardware of military and intelligence networks across the West. In July of 2021, the Commerce Department's Bureau of Industry and Security added the Hangzhou, China-based encryption chip manufacturer Hualan Microelectronics, also known as Sage Microelectronics, to its so-called "Entity List," a vaguely named trade restrictions list that highlights companies "acting contrary to the foreign policy interests of the United States." Specifically, the bureau noted that Hualan had been added to the list for "acquiring and ... attempting to acquire US-origin items in support of military modernization for [China's] People's Liberation Army." Yet nearly two years later, Hualan--and in particular its subsidiary known as Initio, a company originally headquartered in Taiwan that it acquired in 2016--still supplies encryption microcontroller chips to Western manufacturers of encrypted hard drives, including several that list as customers on their websites Western governments' aerospace, military, and intelligence agencies: NASA, NATO, and the US and UK militaries.

The war in Ukraine has entered a new stage; this much we know. In the weeks leading up to the Ukrainian armed forces' long-anticipated counter-offensive, a series of attacks in Moscow and in the Belgorod Region, near the border with Ukraine, marked the most significant incursions into Russia since the full-scale war began. In the Belgorod Region, armed units overran villages and took hostages. In Moscow, two drones were shot down near the Kremlin on May 3rd. Four weeks later, eight drones crashed into residential buildings on the outskirts of the city.

This paper investigates an interference-aware joint path planning and power allocation mechanism for a cellular-connected unmanned aerial vehicle (UAV) in a sparse suburban environment. The UAV's goal is to fly from an initial point and reach a destination point by moving along the cells to guarantee the required quality of service (QoS). In particular, the UAV aims to maximize its uplink throughput and minimize the level of interference to the ground user equipment (UEs) connected to the neighbor cellular BSs, considering the shortest path and flight resource limitation. Expert knowledge is used to experience the scenario and define the desired behavior for the sake of the agent (i.e., UAV) training. To solve the problem, an apprenticeship learning method is utilized via inverse reinforcement learning (IRL) based on both Q-learning and deep reinforcement learning (DRL). The performance of this method is compared to learning from a demonstration technique called behavioral cloning (BC) using a supervised learning approach. Simulation and numerical results show that the proposed approach can achieve expert-level performance. We also demonstrate that, unlike the BC technique, the performance of our proposed approach does not degrade in unseen situations.

Multi-task missions for unmanned aerial vehicles (UAVs) involving inspection and landing tasks are challenging for novice pilots due to the difficulties associated with depth perception and the control interface. We propose a shared autonomy system, alongside supplementary information displays, to assist pilots to successfully complete multi-task missions without any pilot training. Our approach comprises of three modules: (1) a perception module that encodes visual information onto a latent representation, (2) a policy module that augments pilot's actions, and (3) an information augmentation module that provides additional information to the pilot. The policy module is trained in simulation with simulated users and transferred to the real world without modification in a user study (n=29), alongside supplementary information schemes including learnt red/green light feedback cues and an augmented reality display. The pilot's intent is unknown to the policy module and is inferred from the pilot's input and UAV's states. The assistant increased task success rate for the landing and inspection tasks from [16.67% & 54.29%] respectively to [95.59% & 96.22%]. With the assistant, inexperienced pilots achieved similar performance to experienced pilots. Red/green light feedback cues reduced the required time by 19.53% and trajectory length by 17.86% for the inspection task, where participants rated it as their preferred condition due to the intuitive interface and providing reassurance. This work demonstrates that simple user models can train shared autonomy systems in simulation, and transfer to physical tasks to estimate user intent and provide effective assistance and information to the pilot.

Over the past decades, quadcopters have been investigated, due to their mobility and flexibility to operate in a wide range of environments. They have been used in various areas, including surveillance and monitoring. During a mission, drones do not have to remain active once they have reached a target location. To conserve energy and maintain a static position, it is possible to perch and stop the motors in such situations. The problem of achieving a reliable and highly accurate perching method remains a challenge and promising. In this paper, a vision-based autonomous perching approach for nano quadcopters onto a predefined perching target on horizontal surfaces is proposed. First, a perching target with a small marker inside a larger one is designed to improve detection capability at a variety of ranges. Second, a monocular camera is used to calculate the relative poses of the flying vehicle from the markers detected. Then, a Kalman filter is applied to determine the pose more reliably, especially when measurement data is missing. Next, we introduce an algorithm for merging the pose data from multiple markers. Finally, the poses are sent to the perching planner to conduct the real flight test to align the drone with the target's center and steer it there. Based on the experimental results, the approach proved to be effective and feasible. The drone can successfully perch on the center of markers within two centimeters of precision.

Representation learning based on multi-task pretraining has become a powerful approach in many domains. In particular, task-aware representation learning aims to learn an optimal representation for a specific target task by sampling data from a set of source tasks, while task-agnostic representation learning seeks to learn a universal representation for a class of tasks. In this paper, we propose a general and versatile algorithmic and theoretic framework for \textit{active representation learning}, where the learner optimally chooses which source tasks to sample from. This framework, along with a tractable meta algorithm, allows most arbitrary target and source task spaces (from discrete to continuous), covers both task-aware and task-agnostic settings, and is compatible with deep representation learning practices. We provide several instantiations under this framework, from bilinear and feature-based nonlinear to general nonlinear cases. In the bilinear case, by leveraging the non-uniform spectrum of the task representation and the calibrated source-target relevance, we prove that the sample complexity to achieve $\varepsilon$-excess risk on target scales with $ (k^*)^2 \|v^*\|_2^2 \varepsilon^{-2}$ where $k^*$ is the effective dimension of the target and $\|v^*\|_2^2 \in (0,1]$ represents the connection between source and target space. Compared to the passive one, this can save up to $\frac{1}{d_W}$ of sample complexity, where $d_W$ is the task space dimension. Finally, we demonstrate different instantiations of our meta algorithm in synthetic datasets and robotics problems, from pendulum simulations to real-world drone flight datasets. On average, our algorithms outperform baselines by $20\%-70\%$.

Path planning for multiple tethered robots is a challenging problem due to the complex interactions among the cables and the possibility of severe entanglements. Previous works on this problem either consider idealistic cable models or provide no guarantee for entanglement-free paths. In this work, we present a new approach to address this problem using the theory of braids. By establishing a topological equivalence between the physical cables and the space-time trajectories of the robots, and identifying particular braid patterns that emerge from the entangled trajectories, we obtain the key finding that all complex entanglements stem from a finite number of interaction patterns between 2 or 3 robots. Hence, non-entanglement can be guaranteed by avoiding these interaction patterns in the trajectories of the robots. Based on this finding, we present a graph search algorithm using the permutation grid to efficiently search for a feasible topology of paths and reject braid patterns that result in an entanglement. We demonstrate that the proposed algorithm can achieve 100% goal-reaching capability without entanglement for up to 10 drones with a slack cable model in a high-fidelity simulation platform. The practicality of the proposed approach is verified using three small tethered UAVs in indoor flight experiments.

Ahead of Indian Prime Minister Narendra Modi's state visit to Washington, the Biden administration is pushing New Delhi to cut through its own red tape and advance a deal for dozens of Unite States-made armed drones, two people familiar with the matter have told Reuters news agency. India has long expressed interest in buying large armed drones from the US. But bureaucratic stumbling blocks have hampered a deal for SeaGuardian drones, which could be worth $2bn to $3bn, for years. US negotiators are counting on Modi's White House visit on June 22 to seal the deal. Since the date for Modi's visit was fixed, the US State Department, Pentagon and White House have asked India to be able to "show" progress on the deal for as many as 30 armable MQ-9B SeaGuardian drones made by General Atomics, two sources told Reuters.