Unmanned aerial vehicles (UAVs) rely on optical sensors such as cameras and lidar for autonomous operation. However, such optical sensors are error-prone in bad lighting, inclement weather conditions including fog and smoke, and around textureless or transparent surfaces. In this paper, we ask: is it possible to fly UAVs without relying on optical sensors, i.e., can UAVs fly without seeing? We present BatMobility, a lightweight mmWave radar-only perception system for UAVs that eliminates the need for optical sensors. BatMobility enables two core functionalities for UAVs -- radio flow estimation (a novel FMCW radar-based alternative for optical flow based on surface-parallel doppler shift) and radar-based collision avoidance. We build BatMobility using commodity sensors and deploy it as a real-time system on a small off-the-shelf quadcopter running an unmodified flight controller. Our evaluation shows that BatMobility achieves comparable or better performance than commercial-grade optical sensors across a wide range of scenarios.

On the screen in front of me is a mountain range. Moving toward my troops from the top-right corner is an ominous yellow dot. I suspect it's an enemy drone, but it could be a bird or a civilian aircraft, so I ask my long-range camera to home in on it. Within seconds, it returns a snapshot of a wide-winged military drone. The incoming dot turns from yellow to red, signifying a threat.

This letter addresses the problem of trajectory planning in a marsupial robotic system consisting of an unmanned aerial vehicle (UAV) linked to an unmanned ground vehicle (UGV) through a non-taut tether with controllable length. To the best of our knowledge, this is the first method that addresses the trajectory planning of a marsupial UGV-UAV with a non-taut tether. The objective is to determine a synchronized collision-free trajectory for the three marsupial system agents: UAV, UGV, and tether. First, we present a path planning solution based on optimal Rapidly-exploring Random Trees (RRT*) with novel sampling and steering techniques to speed-up the computation. This algorithm is able to obtain collision-free paths for the UAV and the UGV, taking into account the 3D environment and the tether. Then, the letter presents a trajectory planner based on non-linear least squares. The optimizer takes into account aspects not considered in the path planning, like temporal constraints of the motion imposed by limits on the velocities and accelerations of the robots, or raising the tether's clearance. Simulated and field test results demonstrate that the approach generates obstacle-free, smooth, and feasible trajectories for the marsupial system.

Russia launched overnight air attacks on Ukraine's south and east using drones and possibly ballistic missiles, Ukrainian officials said. The southern port of Odesa and the Mykolaiv, Donetsk, Kherson, Zaporizhia and Dnipropetrovsk regions were under threat of Russian drone attacks. Ukraine's air force said it downed 31 out of 36 Iranian-made Shahed kamikaze drones, all six Kalibr cruise missiles and one reconnaissance drone launched by Russia overnight. Russia's defence ministry said it carried out overnight attacks on two Ukrainian port cities in what it called "a mass revenge strike", a day after an attack on the Crimean Bridge. The ministry said in a statement it struck Odesa and Mykolaiv and hit all targets.

Autonomous systems, such as Unmanned Aerial Vehicles (UAVs), are expected to run complex reinforcement learning (RL) models to execute fully autonomous position-navigation-time tasks within stringent onboard weight and power constraints. We observe that reducing onboard operating voltage can benefit the energy efficiency of both the computation and flight mission, however, it can also result in on-chip bit failures that are detrimental to mission safety and performance. To this end, we propose BERRY, a robust learning framework to improve bit error robustness and energy efficiency for RL-enabled autonomous systems. BERRY supports robust learning, both offline and on-board the UAV, and for the first time, demonstrates the practicality of robust low-voltage operation on UAVs that leads to high energy savings in both compute-level operation and system-level quality-of-flight. We perform extensive experiments on 72 autonomous navigation scenarios and demonstrate that BERRY generalizes well across environments, UAVs, autonomy policies, operating voltages and fault patterns, and consistently improves robustness, efficiency and mission performance, achieving up to 15.62% reduction in flight energy, 18.51% increase in the number of successful missions, and 3.43x processing energy reduction.

Accurate navigation is of paramount importance to ensure flight safety and efficiency for autonomous drones. Recent research starts to use Deep Neural Networks to enhance drone navigation given their remarkable predictive capability for visual perception. However, existing solutions either run DNN inference tasks on drones in situ, impeded by the limited onboard resource, or offload the computation to external servers which may incur large network latency. Few works consider jointly optimizing the offloading decisions along with image transmission configurations and adapting them on the fly. In this paper, we propose A3D, an edge server assisted drone navigation framework that can dynamically adjust task execution location, input resolution, and image compression ratio in order to achieve low inference latency, high prediction accuracy, and long flight distances. Specifically, we first augment state-of-the-art convolutional neural networks for drone navigation and define a novel metric called Quality of Navigation as our optimization objective which can effectively capture the above goals. We then design a deep reinforcement learning based neural scheduler at the drone side for which an information encoder is devised to reshape the state features and thus improve its learning ability. To further support simultaneous multi-drone serving, we extend the edge server design by developing a network-aware resource allocation algorithm, which allows provisioning containerized resources aligned with drones' demand. We finally implement a proof-of-concept prototype with realistic devices and validate its performance in a real-world campus scene, as well as a simulation environment for thorough evaluation upon AirSim. Extensive experimental results show that A3D can reduce end-to-end latency by 28.06% and extend the flight distance by up to 27.28% compared with non-adaptive solutions.

The United States is reportedly planning to send Ukraine another $1.3 billion in military aid as it continues a counteroffensive against Russia. The weapons package includes air defenses, counter-drone systems, exploding drones and ammunition, Reuters reported, citing two unnamed U.S. officials. Weapons manufacturers will provide the arms purchased by the United States to Kyiv through President Biden's Ukraine Security Assistance Initiative (USAI) program, so U.S. weapons stocks will not be depleted. Among the systems and ammunition the U.S. plans to buy for Kyiv are counter-air defenses made by L3Harris Technologies called the Vehicle-Agnostic Modular Palletized ISR Rocket Equipment or VAMPIRE, Reuters reported. Military aid, delivered as part of U.S. security assistance to Ukraine, is unloaded at the Boryspil International Airport outside Kyiv, Feb. 13, 2022.

U.S. Air Forces Central said Thursday that a Russian military fighter jet engaged with U.S. aircraft over Syria for 2nd consecutive day. The incident followed a previous clash on Wednesday. A Russian fighter jet flew dangerously close to a manned U.S. surveillance craft in the air over Syria, endangering the lives of all four American crew members, the U.S. military announced Monday. The Sunday flyby was the latest in a series of incidents in which Russian aircraft harassed American warplanes and drones in the air over Syria. A Russian SU-35v approached a U.S. MC-12 in a manner that could have resulted in a fatal accident, officials said.

An aborted armed mutiny by Wagner mercenaries exposed Russia's porous home front, shook Russian President Vladimir Putin's authority and resulted in the removal of thousands of seasoned fighters from the battlefields of Ukraine. Remedying the fallout will be costly, especially with elections looming next March in an economy worn down by almost 17 months of war and sanctions. And in a reminder of the threats Russia now faces, authorities on Monday said two Ukrainian drones caused explosions that killed two people and damaged the symbolic bridge to Crimea, the peninsula Putin annexed from Ukraine in 2014. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

Pawleys Island, South Carolina, Mayor Brian Henry tells "Your World" that Hurricane Ian was different and brought a significant storm surge to the island. A high-tech sailing drone was deployed onto the Atlantic Ocean near Charleston, South Carolina, this past weekend to collect weather data directly from wicked hurricanes. The autonomous ocean drone, known as a saildrone, was redeployed by California-based company Saildrone Inc., which designs and operates autonomous ocean drones, in partnership with the National Oceanic and Atmospheric Administration (NOAA) to assist the agency in data collection on hurricanes. The same saildrone made international headlines in 2021 when it captured the "first-ever video from inside a major hurricane at sea" when Hurricane Sam barreled across the Atlantic. NOAA has previously incorporated drones into its research of hurricanes and 2023 will see an even larger and more high-tech fleet.