Last Prime Day, the DJI Osmo action camera was one of the most popular deals with PopSci readers. This year, the updated version is even cheaper at just 199 for the Essentials bundle. That's a full 50 cheaper than I have seen it all year. Several other DJI products for creatives are also on sale during Prime Day, including a gimbal stabilizer for smartphones and a fancy drone that you can use to shoot aerial videos or freak out your dog. Remember, if you don't have an active Amazon Prime subscription, you can sign up for a free 30-day trial here.

President Donald Trump says the U.S. will have to send more weapons to Ukraine, just days after Pentagon paused critical weapons deliveries to Kyiv. NATO jets were scrambled overnight as Russia carried out its largest drone attack yet on Ukraine, launching more than 700 drones, officials said. Ukrainian President Volodymyr Zelenskyy said the "new massive Russian attack on our cities" involved "728 drones of various types, including over 300 Shaheds, and 13 missiles – Kinzhals and Iskanders. "Most of the targets were shot down. Our interceptor drones were used -- dozens of enemy targets were downed, and we are scaling up this technology.

Perching on structures such as trees, beams, and ledges is essential for extending the endurance of aerial robots by enabling energy conservation in standby or observation modes. A tethered tensile perching mechanism offers a simple, adaptable solution that can be retrofitted to existing robots and accommodates a variety of structure sizes and shapes. However, tethered tensile perching introduces significant modelling challenges which require precise management of aerial robot dynamics, including the cases of tether slack & tension, and momentum transfer. Achieving smooth wrapping and secure anchoring by targeting a specific tether segment adds further complexity. In this work, we present a novel trajectory framework for tethered tensile perching, utilizing reinforcement learning (RL) through the Soft Actor-Critic from Demonstrations (SACfD) algorithm. By incorporating both optimal and suboptimal demonstrations, our approach enhances training efficiency and responsiveness, achieving precise control over position and velocity. This framework enables the aerial robot to accurately target specific tether segments, facilitating reliable wrapping and secure anchoring. We validate our framework through extensive simulation and real-world experiments, and demonstrate effectiveness in achieving agile and reliable trajectory generation for tensile perching.

With the emergence of compute-intensive and delay-sensitive applications in vehicular networks, unmanned aerial vehicles (UAVs) have emerged as a promising complement for vehicular edge computing due to the high mobility and flexible deployment. However, the existing UAV-assisted offloading strategies are insufficient in coordinating heterogeneous computing resources and adapting to dynamic network conditions. Hence, this paper proposes a dual-layer UAV-assisted edge computing architecture based on partial offloading, composed of the relay capability of high-altitude UAVs and the computing support of low-altitude UAVs. The proposed architecture enables efficient integration and coordination of heterogeneous resources. A joint optimization problem is formulated to minimize the system delay and energy consumption while ensuring the task completion rate. To solve the high-dimensional decision problem, we reformulate the problem as a Markov decision process and propose a hierarchical offloading scheme based on the soft actor-critic algorithm. The method decouples global and local decisions, where the global decisions integrate offloading ratios and trajectory planning into continuous actions, while the local scheduling is handled via designing a priority-based mechanism. Simulations are conducted and demonstrate that the proposed approach outperforms several baselines in task completion rate, system efficiency, and convergence speed, showing strong robustness and applicability in dynamic vehicular environments.

President Donald Trump says the U.S. will have to send more weapons to Ukraine, just days after Pentagon paused critical weapons deliveries to Kyiv. President Donald Trump on Monday said that his administration would be sending defensive weapons to Ukraine so the war-torn country could defend itself from Russia's ongoing invasion, an apparent turnaround after the Pentagon said last week it was pausing such deliveries. His comments came as Russian attacks on Ukraine killed at least 11 civilians and injured more than 80 others, including seven children, officials said Monday. "We have to," Trump said when questioned at the start of a dinner he was hosting at the White House for Israeli Prime Minister Benjamin Netanyahu. "They have to be able to defend themselves. They're getting hit very hard now. We're going to send some more weapons -- defensive weapons primarily."

The widespread use of consumer drones has introduced serious challenges for airspace security and public safety. Their high agility and unpredictable motion make drones difficult to track and intercept. While existing methods focus on detecting current positions, many counter-drone strategies rely on forecasting future trajectories and thus require more than reactive detection to be effective. To address this critical gap, we propose an unsupervised vision-based method for predicting the three-dimensional trajectories of drones. Our approach first uses an unsupervised technique to extract drone trajectories from raw LiDAR point clouds, then aligns these trajectories with camera images through motion consistency to generate reliable pseudo-labels. We then combine kinematic estimation with a visual Mamba neural network in a self-supervised manner to predict future drone trajectories. We evaluate our method on the challenging MMAUD dataset, including the V2 sequences that feature wide-field-of-view multimodal sensors and dynamic UAV motion in urban scenes. Extensive experiments show that our framework outperforms supervised image-only and audio-visual baselines in long-horizon trajectory prediction, reducing 5-second 3D error by around 40 percent without using any manual 3D labels. The proposed system offers a cost-effective, scalable alternative for real-time counter-drone deployment. All code will be released upon acceptance to support reproducible research in the robotics community.

Unmanned Aerial Vehicles, operating in environments with relatively few obstacles, offer high maneuverability and full three-dimensional mobility. This allows them to rapidly approach objects and perform a wide range of tasks often challenging for ground robots, making them ideal for exploration, inspection, aerial imaging, and everyday assistance. In this paper, we introduce AirStar, a UAV-centric embodied platform that turns a UAV into an intelligent aerial assistant: a large language model acts as the cognitive core for environmental understanding, contextual reasoning, and task planning. AirStar accepts natural interaction through voice commands and gestures, removing the need for a remote controller and significantly broadening its user base. It combines geospatial knowledge-driven long-distance navigation with contextual reasoning for fine-grained short-range control, resulting in an efficient and accurate vision-and-language navigation (VLN) capability.Furthermore, the system also offers built-in capabilities such as cross-modal question answering, intelligent filming, and target tracking. With a highly extensible framework, it supports seamless integration of new functionalities, paving the way toward a general-purpose, instruction-driven intelligent UAV agent. The supplementary PPT is available at \href{https://buaa-colalab.github.io/airstar.github.io}{https://buaa-colalab.github.io/airstar.github.io}.

Devices operating in Internet of Things (IoT) networks may be deployed across vast geographical areas and interconnected via multi-hop communications. Further, they may be unguarded. This makes them vulnerable to attacks and motivates operators to check on devices frequently. To this end, we propose and study an Unmanned Aerial Vehicle (UAV)-aided attestation framework for use in IoT networks with a charging station powered by solar. A key challenge is optimizing the trajectory of the UAV to ensure it attests as many devices as possible. A trade-off here is that devices being checked by the UAV are offline, which affects the amount of data delivered to a gateway. Another challenge is that the charging station experiences time-varying energy arrivals, which in turn affect the flight duration and charging schedule of the UAV. To address these challenges, we employ a Deep Reinforcement Learning (DRL) solution to optimize the UAV's charging schedule and the selection of devices to be attested during each flight. The simulation results show that our solution reduces the average age of trust by 88% and throughput loss due to attestation by 30%.

A senior United States envoy has praised the Lebanese government's response to a US proposal aimed at disarming Hezbollah amid Israel's continued military presence in the country. Thomas Barrack, an adviser to US President Donald Trump who serves as Washington's ambassador to Turkiye and special envoy for Syria, returned to Beirut on Monday after delivering the US proposal during a June 19 visit. The plan called for the Shia Lebanese group Hezbollah to fully disarm within four months in exchange for a halt to Israeli air strikes and the full withdrawal of Israel's military from the five positions it continues to occupy in southern Lebanon. "What the government gave us was something spectacular in a very short period of time," Barrack told reporters on Monday after meeting Lebanese President Joseph Aoun. "I'm unbelievably satisfied with the response." While Barrack confirmed that he had received a seven-page reply from the Lebanese side, he offered no details on its contents.

United Nations Secretary-General Antonio Guterres has condemned a Russian drone and missile attack against Ukraine this week that has been described as the largest such assault in the three-year war. In a statement on Saturday, Guterres's spokesperson said the Russian strikes "disrupted the power supply to the Zaporizhzhia Nuclear Power Plant, once again underlining the ongoing risks to nuclear safety". "The secretary-general is alarmed by this dangerous escalation and the growing number of civilian casualties," the statement read. Ukrainian officials said Moscow fired more than 500 drones and 11 missiles at the capital Kyiv overnight into Friday in an attack that killed one person, injured at least 23 others and damaged buildings across the city. The sounds of air raid sirens, kamikaze drones and booming detonations reverberated until dawn.