Shortly after a Smartlynx Estonian Airbus 320 took off on February 28, 2018, all four of the aircraft's flight control computers stopped working. Each performed precisely as designed, taking themselves offline after (incorrectly) sensing a fault. The problem, later discovered, was an actuator that had been serviced with oil that was too viscous. A design created to prevent a problem created a problem. Only the skill of the instructor pilot on board prevented a fatal crash.
'Gutfeld!' host is joined by Tyrus Murdoch, Katherine Timpf, Steve Hilton and Joe DeVito to discuss the latest space feat Now that NASA's Ingenuity Mars Helicopter has completed its first test flight on the red planet, members of the agency's Southern California-based Jet Propulsion Laboratory will prepare for the next stages of their mission. Following Monday's historic event, the solar-powered rotorcraft will attempt up to four more flights during a period of fewer than 30 days. Over the next three Martian days -- also known as sols -- the helicopter's team will receive and analyze data and imagery from the first flight and devise a plan for the second experimental test, which is scheduled for no sooner than April 22. "If the helicopter survives the second flight test, the Ingenuity team will consider how best to expand the flight profile," NASA said in a Monday release. Ingenuity will conduct up to five flights, assuming NASA continues to successfully clear potential hurdles, each with chances to record additional data for future use. After Ingenuity is done, the Perseverance rover will resume its focus on surface operations.
NASA's Mars Ingenuity helicopter could finally take to the Martian skies next month after spending its first month strapped to the Perseverance rover while it charges. The US space agency confirmed that the 30 days'test flight window' for the rotorcraft will begin'no earlier than the first week of April'. Ingenuity arrived on Mars strapped to the underside of the NASA Perseverance rover on February 18, following a hair raising '7 minutes of terror' journey to the surface. Before it can make the first flight of an aircraft on the Red Planet, Perseverance needs to'drop it off' in a clear, safe area - likely to happen in the coming weeks. The team behind the 4lb chopper are narrowing down on a launch site, that will become the first'airfield on another world' when Ingenuity makes its maiden flight. The space agency confirmed that the 30 days'test flight window' for the rotorcraft will begin'no earlier than the first week of April' NASA is set to fly where no one has flown before – Mars' atmosphere.
Delseny, Hervé, Gabreau, Christophe, Gauffriau, Adrien, Beaudouin, Bernard, Ponsolle, Ludovic, Alecu, Lucian, Bonnin, Hugues, Beltran, Brice, Duchel, Didier, Ginestet, Jean-Brice, Hervieu, Alexandre, Martinez, Ghilaine, Pasquet, Sylvain, Delmas, Kevin, Pagetti, Claire, Gabriel, Jean-Marc, Chapdelaine, Camille, Picard, Sylvaine, Damour, Mathieu, Cappi, Cyril, Gardès, Laurent, De Grancey, Florence, Jenn, Eric, Lefevre, Baptiste, Flandin, Gregory, Gerchinovitz, Sébastien, Mamalet, Franck, Albore, Alexandre
Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc. It is also an opportunity to implement and embed new capabilities out of the reach of classical implementation techniques. However, ML techniques introduce new potential risks. Therefore, they have only been applied in systems where their benefits are considered worth the increase of risk. In practice, ML techniques raise multiple challenges that could prevent their use in systems submitted to certification constraints. But what are the actual challenges? Can they be overcome by selecting appropriate ML techniques, or by adopting new engineering or certification practices? These are some of the questions addressed by the ML Certification 3 Workgroup (WG) set-up by the Institut de Recherche Technologique Saint Exup\'ery de Toulouse (IRT), as part of the DEEL Project.
The Civil Aviation Safety Authority (CASA), alongside Airservices Australia, on Wednesday announced a trial of a new digital, automated process that is aimed at expediting the approval processes of remotely piloted aircraft operations. According to the organisations, the application process currently takes weeks to complete before commercial drone operators are allowed to take flight. With the trial, CASA and Airservices hope to create an application process that reduces the time required from weeks to seconds. "Moving to digital approval processes is a key initiative for CASA, streamlining interactions and making it easier for operators," CASA acting-CEO and Aviation Safety director Graeme Crawford said. The trial digital process will be delivered through CASA's remotely piloted aircraft systems digital platform, with Airservices and the Queensland University of Technology to develop designated maps that will be used to conduct the relevant analysis required for these automated authorisations.
This paper proposes a new machine learning based system for forest fire earlier detection in a low-cost and accurate manner. Accordingly, it is aimed to bring a new and definite perspective to visual detection in forest fires. A drone is constructed for this purpose. The microcontroller in the system has been programmed by training with deep learning methods, and the unmanned aerial vehicle has been given the ability to recognize the smoke, the earliest sign of fire detection. The common problem in the prevalent algorithms used in fire detection is the high false alarm and overlook rates. Confirming the result obtained from the visualization with an additional supervision stage will increase the reliability of the system as well as guarantee the accuracy of the result. Due to the mobile vision ability of the unmanned aerial vehicle, the data can be controlled from any point of view clearly and continuously. System performance are validated by conducting experiments in both simulation and physical environments.
Drones have always been a high-flying success at the annual CES show. The latest drone with a buzz: Sony's Airpeak drone, which promises to be an eye-in-the-sky for filmmakers. Sony did not offer a lot of information about the drone, but showed a video of it – outfitted with a Sony Alpha 7S III camera – tracking the electronics company's in-development Vision-S electric high-tech vehicle from above. Captured was stunning footage of the snowy, wooded mountainous Austrian landscape. The Airpeak, Sony says, is the smallest class of drones that can carry such a camera.
Recent technological progress in the development of Unmanned Aerial Vehicles (UAVs) together with decreasing acquisition costs make the application of drone fleets attractive for a wide variety of tasks. In agriculture, disaster management, search and rescue operations, commercial and military applications, the advantage of applying a fleet of drones originates from their ability to cooperate autonomously. Multi-Agent Reinforcement Learning approaches that aim to optimize a neural network based control policy, such as the best performing actor-critic policy gradient algorithms, struggle to effectively back-propagate errors of distinct rewards signal sources and tend to favor lucrative signals while neglecting coordination and exploitation of previously learned similarities. We propose a Multi-Critic Policy Optimization architecture with multiple value estimating networks and a novel advantage function that optimizes a stochastic actor policy network to achieve optimal coordination of agents. Consequently, we apply the algorithm to several tasks that require the collaboration of multiple drones in a physics-based reinforcement learning environment. Our approach achieves a stable policy network update and similarity in reward signal development for an increasing number of agents. The resulting policy achieves optimal coordination and compliance with constraints such as collision avoidance.
This week's news swung between some ridiculous extremes. CD Projekt Red released Cyberpunk 2077 without addressing many of the issues people had with its design choices, marketing, development crunch or performance on older consoles -- and still set records. At least a patch is rolling out now to address a feature that could induce seizures, but that seems like a pretty low place to set the bar. SpaceX had to scrub a test flight at the last second, then launched it the next day and successfully pulled off a breathtaking Starship maneuver… before seeing the vehicle explode while attempting to land. Oh, and while SpaceX cleaned up wreckage, the supports holding up its next prototype failed, with SN9 showing minor damage while leaning against a wall.
Humans might not have much involvement in mid-air refueling before long. Boeing has flown a test version of its MQ-25 tanker drone with a refueling pod attached for the first time, taking it one step closer to topping up military aircraft. The 2.5-hour flight showed that the autonomous drone's aerodynamics were sound with the wing-mounted pod it's expected to carry much of the time. The test drone, T1, is a precursor to an "engineering development" model that will take Boeing one step closer to a finished vehicle. This could be a crucial machine.