Daniel Faggella: So, Lorena, I want to be able to dive into these various use cases of how artificial intelligence can start to unlock the value of data in the oil and gas space, and make this really tangible. I know the first category we wanted to talk about was really around the value of subsurface data, that there's a lot of subsurface data, obviously in the oil and oil and gas domain. Lorena Pelegrín: And we see that AI or our ML can help these teams find the data and process the data much, much faster. Yeah, and I imagine a good deal of this has to do with, tell me if I'm wrong here, Lorena, but having an understanding of your company from working with you guys for a little while, I would imagine that the digitization of these myriad, somewhat chunky paper forms is one part of the process here, using some kind of optical character recognition stuff and working with historical records and maybe congealing and digitizing that. Daniel Faggella: But you let me know, Lorena, where does M&A, where does this data come in, in terms of the real value for potential M&A? Daniel Faggella: So Drone Deploy, for example, was on talking about what they do in the energy space with drones and video data to look at and inspect assets.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Arnold Schwarzenegger could've seen this one coming. After a United Nations commission to block killer robots was shut down in 2018, a new report from the international body now says the Terminator-like drones are now here. Last year "an autonomous weaponized drone hunted down a human target last year" and attacked them without being specifically ordered to, according to a report from the UN Security Council's Panel of Experts on Libya, published in March 2021 that was published in the New Scientist magazine and the Star.

Turkey aims to be among the first countries to have an entirely artificial intelligence (AI)-controlled unmanned warplane, with plans for it to take to the Turkish skies in 2023, President Recep Tayyip Erdoğan said Wednesday. The success of Turkish unmanned aerial vehicles (UAV) in the field has produced results that "require war strategies to be rewritten," the president said. Erdoğan was speaking at the ruling Justice and Development Party's (AK Party) parliamentary group meeting in the capital Ankara. The president added that currently a total of 180 Bayraktar TB2 unmanned combat aerial vehicles (UCAVs) are operated in four countries, including Turkey. Previously, Turkish drone magnate Baykar's Chief Technology Officer Selçuk Bayraktar said the maiden flight of the prototype of the country's domestically-made unmanned fighter jet is scheduled for 2023.

Autonomous drones may have attacked humans for the first time ever, according to a United Nations report. Last year, rebels in Libya were bombarded by'unmanned combat aerial vehicles and lethal autonomous weapons systems,' the report alleges. The drones can be operated manually but in this encounter they were self-guided, using on-board cameras and machine learning to find and target enemies. No deaths were confirmed but the drones carry explosive charges and similar systems have caused'significant casualties' in other encounters. According to the March report from the United Nations Security Council's Panel of Experts on Libya, Kargu-2 quadcopters were deployed in the North African nation in March 2020.

Drones have become a common tool, which is utilized in many tasks such as aerial photography, surveillance, and delivery. However, operating a drone requires more and more interaction with the user. A natural and safe method for Human-Drone Interaction (HDI) is using gestures. In this paper, we introduce an HDI framework building upon skeleton-based pose estimation. Our framework provides the functionality to control the movement of the drone with simple arm gestures and to follow the user while keeping a safe distance. We also propose a monocular distance estimation method, which is entirely based on image features and does not require any additional depth sensors. To perform comprehensive experiments and quantitative analysis, we create a customized testing dataset. The experiments indicate that our HDI framework can achieve an average of 93.5\% accuracy in the recognition of 11 common gestures. The code is available at: https://github.com/Zrrr1997/Pose2Drone

Autonomous missions of small unmanned aerial vehicles (UAVs) are prone to collisions owing to environmental disturbances and localization errors. Consequently, a UAV that can endure collisions and perform recovery control in critical aerial missions is desirable to prevent loss of the vehicle and/or payload. We address this problem by proposing a novel foldable quadrotor system which can sustain collisions and recover safely. The quadrotor is designed with integrated mechanical compliance using a torsional spring such that the impact time is increased and the net impact force on the main body is decreased. The post-collision dynamics is analysed and a recovery controller is proposed which stabilizes the system to a hovering location without additional collisions. Flight test results on the proposed and a conventional quadrotor demonstrate that for the former, integrated spring-damper characteristics reduce the rebound velocity and lead to simple recovery control algorithms in the event of unintended collisions as compared to a rigid quadrotor of the same dimension.

This paper presents a vision-based modularized drone racing navigation system that uses a customized convolutional neural network (CNN) for the perception module to produce high-level navigation commands and then leverages a state-of-the-art planner and controller to generate low-level control commands, thus exploiting the advantages of both data-based and model-based approaches. Unlike the state-of-the-art method which only takes the current camera image as the CNN input, we further add the latest three drone states as part of the inputs. Our method outperforms the state-of-the-art method in various track layouts and offers two switchable navigation behaviors with a single trained network. The CNN-based perception module is trained to imitate an expert policy that automatically generates ground truth navigation commands based on the pre-computed global trajectories. Owing to the extensive randomization and our modified dataset aggregation (DAgger) policy during data collection, our navigation system, which is purely trained in simulation with synthetic textures, successfully operates in environments with randomly-chosen photorealistic textures without further fine-tuning.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Drones and cookies apparently work well together. In April, Fox News reported that some Girl Scout troops across the country were having trouble with their cookie sales due to the pandemic. In Virginia, however, some members decided to try using drones to bring the popular cookies to customers.

In this article, we study the problem of air-to-ground ultra-reliable and low-latency communication (URLLC) for a moving ground user. This is done by controlling multiple unmanned aerial vehicles (UAVs) in real time while avoiding inter-UAV collisions. To this end, we propose a novel multi-agent deep reinforcement learning (MADRL) framework, coined a graph attention exchange network (GAXNet). In GAXNet, each UAV constructs an attention graph locally measuring the level of attention to its neighboring UAVs, while exchanging the attention weights with other UAVs so as to reduce the attention mismatch between them. Simulation results corroborates that GAXNet achieves up to 4.5x higher rewards during training. At execution, without incurring inter-UAV collisions, GAXNet achieves 6.5x lower latency with the target 0.0000001 error rate, compared to a state-of-the-art baseline framework.

Federal agencies are scrambling to address a surge in the use of consumer drones as the unmanned aircraft crowd the airspace above critical sites, posing a threat to public safety and national security. The Federal Aviation Administration and the National Aeronautics and Space Administration are developing a joint national air-traffic-control system for low-flying drones. The Department of Homeland Security is testing technologies to detect small drones favored by consumers, and the Pentagon is researching methods to knock them out of the sky. Reports of drone sightings around airports are pouring into the FAA at a rate of more than 100 a month. Commercial pilots flying into and out of Los Angeles International Airport have reported increased sightings of drones near their flight path, with 23 sightings reported to the control tower so far this year, according to an airport official.