The world's largest plane, Stratolaunch, has a completed a key taxi test ahead of taking to the skies for the first time. The gigantic plane, which is the vision of late Microsoft co-founder Paul Allen, is believed to be close to its first flight after reaching a record-breaking 90mph during medium-speed taxi testing at the Mojave Air & Space Port. Allen died Monday in Seattle from complications of non-Hodgkin's lymphoma, aged 65. The massive plane has a wingspan longer than a football field and comes equipped with two cockpits, 28 wheels and six engines normally used to power 747 jumbo jets. Eventually it will be used to transport rockets carrying satellites and even a newly revealed manned space plane into the Earth's upper atmosphere, where they will blast off into space.
CHICAGO – Boeing Co. is creating a new unit to focus on technology that's seemingly straight out of science fiction, including super-fast computing that mimics the synapses of the human brain and hack-proof communications links based on applied quantum physics. So-called neuromorphic processing and quantum communications, two of the futuristic technologies Boeing wants to explore, may seem an odd fit for the world's largest plane-maker. But such concepts increasingly form the core of aerospace innovation, like the networks that may one day manage millions of airborne drones, said Greg Hyslop, Boeing's chief technology officer. The technology being developed around advanced computing and sensors is going to have a "profound impact" on Boeing, Hyslop said in an interview Wednesday. "We thought it's time to do this."
Teams of autonomous unmanned aircraft can be used to monitor wildfires, enabling firefighters to make informed decisions. However, controlling multiple autonomous fixed-wing aircraft to maximize forest fire coverage is a complex problem. The state space is high dimensional, the fire propagates stochastically, the sensor information is imperfect, and the aircraft must coordinate with each other to accomplish their mission. This work presents two deep reinforcement learning approaches for training decentralized controllers that accommodate the high dimensionality and uncertainty inherent in the problem. The first approach controls the aircraft using immediate observations of the individual aircraft. The second approach allows aircraft to collaborate on a map of the wildfire's state and maintain a time history of locations visited, which are used as inputs to the controller. Simulation results show that both approaches allow the aircraft to accurately track wildfire expansions and outperform an online receding horizon controller. Additional simulations demonstrate that the approach scales with different numbers of aircraft and generalizes to different wildfire shapes.
Abstract-- Small unmanned aircraft can help firefighters combat wildfires by providing real-time surveillance of the growing fires. However, guiding the aircraft autonomously given only wildfire images is a challenging problem. This work models noisy images obtained from on-board cameras and proposes two approaches to filtering the wildfire images. The first approach uses a simple Kalman filter to reduce noise and update a belief map in observed areas. The second approach uses a particle filter to predict wildfire growth and uses observations to estimate uncertainties relating to wildfire expansion. The belief maps are used to train a deep reinforcement learning controller, which learns a policy to navigate the aircraft to survey the wildfire while avoiding flight directly over the fire. Simulation results show that the proposed controllers precisely guide the aircraft and accurately estimate wildfire growth, and a study of observation noise demonstrates the robustness of the particle filter approach.
Verisk is now offering customers free access to Geomni's high-resolution aerial imagery following major loss events. Geomni will provide online access to before-and-after imagery of structures and properties inside the catastrophe area. This will enable viewers of the imagery the ability to better understand which areas have been affected by a particular major event and the extent of the damage. According to Geomni President Jeffrey C. Taylor, the information can quickly provide insights for experts who are deploying resources, setting loss reserves, verifying internal estimates, and more. Geomni has a built a library of high-resolution imagery and 3D data by using multiple remote sensing platforms with a focus on fixed-wing aircraft but also including satellites, unmanned aerial vehicles (UAVs), and even mobile devices used to collect ground-level data through the Geomni Mobile app.
For the operators of large helicopters, the principle of big data is nothing new. For years, these companies and their associated MRO operations have been collecting and analyzing vibration data from onboard health and usage monitoring systems (HUMS), looking for potential issues within an aircraft's dynamic systems as well as clues to potential maintenance problems. However, in the current era of analytics, artificial intelligence (AI) and algorithms, new uses for the data coming off the helicopters are being enabled and helping to democratize the use of systems like HUMS. "Today in the helicopter world, a lot of things are being done in the maintenance world as they would have been 40-50 years ago," says Matthieu Louvot, executive vice president for customer support and services at Airbus Helicopters. "Now is the time to digitize."
The Top Gun pilots of the future may use radical virtual instruments instead of the traditional dials and levers. The radical system will monitor a pilot's every move, tracking their gaze and brainwaves to work out exactly what they are looking at - and predicting what they want to do next. Experts at BAE Systems say their'mindreading' technology will enable pilots to control the fighter jet of the future with the blink of an eye. The radical system will monitor a pilot's every move, tracking their gaze to work out exactly what they are looking at - and predicting what they want to do next The system would use the same augmented reality technologies being developed by tech firms to create consumer glasses that can project data on top of the real world, with Apple, Google and others all hard at work on systems. The radical system will monitor a pilot's every move, tracking their gaze and brainwaves to work out exactly what they are looking at - and predicting what they want to do next.
In order to achieve a good level of autonomy in unmanned helicopters, an accurate replication of vehicle dynamics is required, which is achievable through precise mathematical modeling. This paper aims to identify a parametric state-space system for an unmanned helicopter to a good level of accuracy using Invasive Weed Optimization (IWO) algorithm. The flight data of Align TREX 550 flybarless helicopter is used in the identification process. The rigid-body dynamics of the helicopter is modeled in a state-space form that has 40 parameters, which serve as control variables for the IWO algorithm. The results after 1000 iterations were compared with the traditionally used Prediction Error Minimization (PEM) method and also with Genetic Algorithm (GA), which serve as references. Results show a better level of correlation between the actual and estimated responses of the system identified using IWO to that of PEM and GA.
Chennai: An indigenously designed and developed landing gear for the Unmanned Aerial Vehicle (UAV)-Rustom II has been successfully tested on Thursday, a defence statement said. The landing gear developed by a Defence Research and Development Organisation (DRDO) laboratory here has undergone low-speed and high-speed taxi trial in Chitradurga, Karnataka, it said. The maiden flight of Rustom II with the indigenously developed gear was successfully carried out. "The Combat Vehicles Research and Development Establishment (CVRDE), the main laboratory of DRDO, has designed and developed the gear," the statement said. Rustom II is a medium-altitude long-endurance UAV designed for carrying out surveillance for the armed forces.