An oil tanker associated with an Israeli billionaire has been struck by a bomb-carrying drone off the coast of Oman amid heightened tensions with Iran, an official has told the Associated Press. The attack happened on Tuesday night off the coast of Oman, the Middle East-based defence official said. The official spoke on Wednesday on condition of anonymity as they did not have authorisation to discuss the attack publicly. The United Kingdom Maritime Trade Operations, a British military organisation in the region monitoring shipping, told the AP: "We are aware of an incident and it's being investigated at this time." The official identified the vessel attacked as the Liberian-flagged oil tanker Pacific Zircon.

Cargo drones are designed to carry payloads with predefined shape, size, and/or mass. This lack of flexibility requires a fleet of diverse drones tailored to specific cargo dimensions. Here we propose a new reconfigurable drone based on a modular design that adapts to different cargo shapes, sizes, and mass. We also propose a method for the automatic generation of drone configurations and suitable parameters for the flight controller. The parcel becomes the drone's body to which several individual propulsion modules are attached. We demonstrate the use of the reconfigurable hardware and the accompanying software by transporting parcels of different mass and sizes requiring various numbers and propulsion modules' positioning. The experiments are conducted indoors (with a motion capture system) and outdoors (with an RTK-GNSS sensor). The proposed design represents a cheaper and more versatile alternative to the solutions involving several drones for parcel transportation.

Military drones are taking an increasingly prominent role in armed conflict, and the use of multiple drones in a swarm can be useful. Who the drivers of the research are and what sub-domains exist is analyzed and visually presented in this research using NLP techniques based on 946 studies. Most research is conducted in the Western world, led by the United States, the United Kingdom, and Germany. Through Tf-idf scoring, it is shown that countries have significant differences in the subdomains studied. Overall, 2019 and 2020 saw the most works published, with significant interest in military swarm drones as early as 2008. This study provides a first glimpse into research in this area and prompts further investigation.

Network slicing is a well assessed approach enabling virtualization of the mobile core and radio access network (RAN) in the emerging 5th Generation New Radio. Slicing is of paramount importance when dealing with the emerging and diverse vertical applications entailing heterogeneous sets of requirements. 5G is also envisioning Unmanned Aerial Vehicles (UAVs) to be a key element in the cellular network standard, aiming at their use as aerial base stations and exploiting their flexible and quick deployment to enhance the wireless network performance. This work presents a UAV-assisted 5G network, where the aerial base stations (UAV-BS) are empowered with network slicing capabilities aiming at optimizing the Service Level Agreement (SLA) satisfaction ratio of a set of users. The users belong to three heterogeneous categories of 5G service type, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC). A first application of multi-agent and multi-decision deep reinforcement learning for UAV-BS in a network slicing context is introduced, aiming at the optimization of the SLA satisfaction ratio of users through the joint allocation of radio resources to slices and refinement of the UAV-BSs 2-dimensional trajectories. The performance of the presented strategy have been tested and compared to benchmark heuristics, highlighting a higher percentage of satisfied users (at least 27% more) in a variety of scenarios.

Amazon has unveiled its newest delivery drone that will soon be dropping packages from 12 feet in the air in two U.S. cities. The retail giant has long wanted to solve the last leg of package delivery, especially since it launched Amazon Prime's Two-Day delivery offering in 2005. Jeff Bezos first announced drone delivery in 2013, but the service only made a single delivery three years after that. The drone, dubbed MK27-2, will start making deliveries in Lockeford, California, and College Station, Texas, by the end of 2022. The autonomous craft is about five-and-a-half feet in diameter, weighs 80 pounds and can only carry packages that weight less than five pounds.

The next chapter of the robotics revolution is well underway with the deployment of robots for a broad range of commercial use-cases. Even in a myriad of applications and environments, there exists a common vocabulary of components that robots share - the need for a modular, scalable, and reliable architecture; sensing; planning; mobility; and autonomy. The Robot Operating System (ROS) was an integral part of the last chapter, demonstrably expediting robotics research with freely-available components and a modular framework. However, ROS 1 was not designed with many necessary production-grade features and algorithms. ROS 2 and its related projects have been redesigned from the ground up to meet the challenges set forth by modern robotic systems in new and exploratory domains at all scales. In this review, we highlight the philosophical and architectural changes of ROS 2 powering this new chapter in the robotics revolution. We also show through case studies the influence ROS 2 and its adoption has had on accelerating real robot systems to reliable deployment in an assortment of challenging environments.

We approach autonomous drone-based reforestation with a collaborative multi-agent reinforcement learning (MARL) setup. Agents can communicate as part of a dynamically changing network. We explore collaboration and communication on the back of a high-impact problem. Forests are the main resource to control rising CO2 conditions. Unfortunately, the global forest volume is decreasing at an unprecedented rate. Many areas are too large and hard to traverse to plant new trees. To efficiently cover as much area as possible, here we propose a Graph Neural Network (GNN) based communication mechanism that enables collaboration. Agents can share location information on areas needing reforestation, which increases viewed area and planted tree count. We compare our proposed communication mechanism with a multi-agent baseline without the ability to communicate. Results show how communication enables collaboration and increases collective performance, planting precision and the risk-taking propensity of individual agents.

We introduce Air Learning, an open-source simulator, and a gym environment for deep reinforcement learning research on resource-constrained aerial robots. Equipped with domain randomization, Air Learning exposes a UAV agent to a diverse set of challenging scenarios. We seed the toolset with point-to-point obstacle avoidance tasks in three different environments and Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) trainers. Air Learning assesses the policies' performance under various quality-of-flight (QoF) metrics, such as the energy consumed, endurance, and the average trajectory length, on resource-constrained embedded platforms like a Raspberry Pi. We find that the trajectories on an embedded Ras-Pi are vastly different from those predicted on a high-end desktop system, resulting in up to 40% longer trajectories in one of the environments. To understand the source of such discrepancies, we use Air Learning to artificially degrade high-end desktop performance to mimic what happens on a low-end embedded system. We then propose a mitigation technique that uses the hardware-in-the-loop to determine the latency distribution of running the policy on the target platform (onboard compute on the aerial robot). A randomly sampled latency from the latency distribution is then added as an artificial delay within the training loop. Training the policy with artificial delays allows us to minimize the hardware gap (discrepancy in the flight time metric reduced from 37.73% to 0.5%). Thus, Air Learning with hardware-in-the-loop characterizes those differences and exposes how the onboard compute's choice affects the aerial robot's performance. We also conduct reliability studies to assess the effect of sensor failures on the learned policies. All put together, Air Learning enables a broad class of deep RL research on UAVs. The source code is available at:http://bit.ly/2JNAVb6.

In the wake of storms of the near future, swarms of drones could replace helicopters and planes, providing emergency crews with more rapid and accurate data on the coming threats of lightning-sparked bushfires or flash floods heading for homes. Authorities now rely on satellites, which require clear weather during daytime and may only provide resolution down to 10 metres. Alternatively, pilots of aircraft may burn as much as $3,400 worth of fuel an hour and often can't fly for safety reasons. Enter firms such as Sydney-based Carbonix, a developer that started out designing America's Cup racing yachts before changing tack to make drones capable of flying eight hours or longer with resolution fine enough to read words on a piece of paper. Dario Valenza, chief technology officer and founder of Carbonix, says thermal cameras on the drones could quickly verify fires started by lightning in remote regions, helping to direct fire crews to the scene "with only a few per cent of the fuel" used by conventional aircraft that might have their operations curtailed by weather.

Ukrainian President Volodymyr Zelenskyy has backed a fundraising campaign to help Ukraine build a naval drone fleet to protect cities against Russian missiles launched from warships on the Black Sea. United24, an initiative Zelenskyy launched to raise charitable donations following Russia's invasion in February, said Ukraine needed 100 drones operating from the sea, each of which costs 10 million hryvnias (around $274,000). The fundraising site said that since the invasion began, Russian has launched over 4,500 missiles into Ukraine and "every fifth strike came from the sea". "We must defend the waters of our seas and peaceful cities from Russian missiles launched from ships," Zelenskyy wrote on the Telegram messaging app on Friday. "Naval drones will also help unblock the corridor for civilian ships transporting grain for the world," he said.