Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. French officials on Monday said investigators have obtained cockpit voice data from the black boxes of the Ukrainian jet mistakenly shot down by Iran in January. "CVR data - including the event itself - has been successfully downloaded," wrote France's BEA investigation bureau said in a tweet. The deadly incident came during a period of heightened tensions with the U.S. -- just hours after Iran fired missiles at Iraqi airbases housing American troops in retaliation for a U.S. drone strike that killed Iran's top military commander, Gen. Qassim Soleimani, earlier in January.

There's a good chance you've seen a drone swarm. Maybe not in person, but probably televised during a New Year's celebration. A drone swarm occurs when a large number of the flying robots take to the skies in sync. It isn't a coincidence that they almost always fly in open outdoor areas. For these robotic fliers, it can be difficult to navigate in tight spaces without running into each other or environmental obstacles.

The global Artificial Intelligence (AI) -based Drone Software market size is expected to continue its rapid growth through the next five years, according to several reports. A Research And Markets report said that: "Digital industries are now implementing AI in their devices to improve in their fields across the globe. Application of AI in drone is one such advancement which has brought a revolutionary change in the operations of the industries. AI enables storing and managing the data in bulk which enables the drones to give better performance. The application of AI can enable the drones to function as per the user's command and with longer distance coverage. In addition, AI integrated drone enables the industries to keep a bird-eye view of the land for vigilance & mapping purpose. The increased income levels have brought up new demands that have resulted in increasing supply of goods. Manufacturers are bringing in new features by implementing AI in their devices such as mobiles so ...

Advances in hardware technology have facilitated more integration of sophisticated software toward augmenting the development of Unmanned Vehicles (UVs) and mitigating constraints for onboard intelligence. As a result, UVs can operate in complex missions where continuous trans-formation in environmental condition calls for a higher level of situational responsiveness and autonomous decision making. This book is a research monograph that aims to provide a comprehensive survey of UVs autonomy and its related properties in internal and external situation awareness to-ward robust mission planning in severe conditions. An advance level of intelligence is essential to minimize the reliance on the human supervisor, which is a main concept of autonomy. A self-controlled system needs a robust mission management strategy to push the boundaries towards autonomous structures, and the UV should be aware of its internal state and capabilities to assess whether current mission goal is achievable or find an alternative solution. In this book, the AUVs will become the major case study thread but other cases/types of vehicle will also be considered. In-deed the research monograph, the review chapters and the new approaches we have developed would be appropriate for use as a reference in upper years or postgraduate degrees for its coverage of literature and algorithms relating to Robot/Vehicle planning, tasking, routing, and trust.

When considering transformational ways to use computer vision on the edge in devices like robots, drones, cameras, and other devices, Booz Allen Hamilton VP Josh Sullivan advises caution, urging people to take security seriously on what's become a whole new attack vector. "For me, deploying an AI model in your IT environment is an entirely new attack vector. I've seen a model working correctly that can identify tanks and other military equipment be fooled into seeing a school bus because someone sent poisoned data into the model," he said. Failure to keep models secure can lead to adversarial machine learning attacks to make malicious code appear as benign or a range of other bad outcomes. Sullivan was joined in conversation at VentureBeat's Transform 2020 conference by Nvidia VP of federal initiatives Anthony Robbins, Intel IoT VP Stacey Shulman, and Joint AI Center acting director Nand Mulchandani.

The global Artificial Intelligence (AI) -based Drone Software market size is expected to continue its rapid growth through the next five years, according to several reports. A Research And Markets report said that: "Digital industries are now implementing AI in their devices to improve in their fields across the globe. Application of AI in drone is one such advancement which has brought a revolutionary change in the operations of the industries. AI enables storing and managing the data in bulk which enables the drones to give better performance. The application of AI can enable the drones to function as per the user's command and with longer distance coverage. In addition, AI integrated drone enables the industries to keep a bird-eye view of the land for vigilance & mapping purpose. The increased income levels have brought up new demands that have resulted in increasing supply of goods. Manufacturers are bringing in new features by implementing AI in their devices such as mobiles so ...

PALM BEACH, Florida, July 16, 2020 /PRNewswire/ — The global Artificial Intelligence (AI) -based Drone Software market size is expected to continue …

Farmland bird species are declining over most of Europe. Birds breeding on the ground are particularly vulnerable because they are exposed to mechanical operations, like plowing and sowing, which take place in spring and often accidentally destroy nests. Researchers flew a drone carrying a thermal camera over agricultural fields to record images. These were then fed to an artificial intelligence algorithm capable of accurately identifying nests, a first step to aid their protection. Researchers tested the system in Southern Finland near University of Helsinki's Lammi Biological Station, using wild nests with eggs of the Lapwing Vanellus vanellus.

In this paper, we present a novel developmental reinforcement learning-based controller for a quadcopter with thrust vectoring capabilities. This multirotor UAV design has tilt-enabled rotors. It utilizes the rotor force magnitude and direction to achieve the desired state during flight. The control policy of this robot is learned using the policy transfer from the learned controller of the quadcopter (comparatively simple UAV design without thrust vectoring). This approach allows learning a control policy for systems with multiple inputs and multiple outputs. The performance of the learned policy is evaluated by physics-based simulations for the tasks of hovering and way-point navigation. The flight simulations utilize a flight controller based on reinforcement learning without any additional PID components. The results show faster learning with the presented approach as opposed to learning the control policy from scratch for this new UAV design created by modifications in a conventional quadcopter, i.e., the addition of more degrees of freedom (4-actuators in conventional quadcopter to 8-actuators in tilt-rotor quadcopter). We demonstrate the robustness of our learned policy by showing the recovery of the tilt-rotor platform in the simulation from various non-static initial conditions in order to reach a desired state. The developmental policy for the tilt-rotor UAV also showed superior fault tolerance when compared with the policy learned from the scratch. The results show the ability of the presented approach to bootstrap the learned behavior from a simpler system (lower-dimensional action-space) to a more complex robot (comparatively higher-dimensional action-space) and reach better performance faster.

This paper studies the problem of information freshness-aware task offloading in an air-ground integrated multi-access edge computing system, which is deployed by an infrastructure provider (InP). A third-party real-time application service provider provides computing services to the subscribed mobile users (MUs) with the limited communication and computation resources from the InP based on a long-term business agreement. Due to the dynamic characteristics, the interactions among the MUs are modelled by a non-cooperative stochastic game, in which the control policies are coupled and each MU aims to selfishly maximize its own expected long-term payoff. To address the Nash equilibrium solutions, we propose that each MU behaves in accordance with the local system states and conjectures, based on which the stochastic game is transformed into a single-agent Markov decision process. Moreover, we derive a novel online deep reinforcement learning (RL) scheme that adopts two separate double deep Q-networks for each MU to approximate the Q-factor and the post-decision Q-factor. Using the proposed deep RL scheme, each MU in the system is able to make decisions without a priori statistical knowledge of dynamics. Numerical experiments examine the potentials of the proposed scheme in balancing the age of information and the energy consumption.