Drone racing is an increasingly popular sport with big money prizes for skilled professionals. New control algorithms developed at the University of Zurich (UZH) have beaten experienced human pilots for the first time – but they still have significant limitations. In the past, attempts to develop automated algorithms to beat humans have run into problems with accurately simulating the limitations of the quadcopter and the flight path it takes. Traditional flight paths around a complex drone racing course are calculated using polynomial methods which produce a series of smooth curves, and these are not necessarily as fast as the sharper and more jagged paths flown by human pilots. A team from the Robotics and Perception Group at UZH has developed a trajectory planning algorithm to calculates the optimal route at every point in the flight, rather than doing it section by section.

There may not have been any fans in the Olympic Stadium, but Japan still found a way to put on a show for the opening of the 2020 Summer Games. The host country charmed early with the parade of nations, which featured an orchestrated video game soundtrack, and then showed off the type of creativity it's known for with a performance involving the Olympic pictograms. But Tokyo saved the biggest spectacle for last. Towards the end of the ceremony, a fleet of 1,824 drones took to the skies above the Olympic Stadium. Initially arrayed in the symbol of the 2020 Games, they then took on the shape of the Earth before a rendition of John Lenon's "Imagine," which was reworked by Hans Zimmer for the Olympics, played across the stadium.

Zala Aero, a Russian UAV manufacturer, presented its state-of-the-art vertical takeoff and landing drone – the ZALA VTOL – at the MAKS 2021 International Aviation and Space Salon, the company told reporters during the air show. "The ZALA VTOL combines the properties of an airplane type drone and a tilt-rotor aircraft. The flight configuration changes depending on the assigned mission. The electric propulsion system enables the aircraft to be in the air for up to 4 hours, providing a range of up to 200 km in aircraft configuration," the company said. The built-in on-board computer ZX1 based on artificial intelligence makes it possible to process Full HD format data and transmit HD videos and photos over encrypted communication links to a ground control station.

To be useful, drones need to be quick. Because of their limited battery life they must complete whatever task they have – searching for survivors on a disaster site, inspecting a building, delivering cargo – in the shortest possible time. And they may have to do it by going through a series of waypoints like windows, rooms, or specific locations to inspect, adopting the best trajectory and the right acceleration or deceleration at each segment. The best human drone pilots are very good at doing this and have so far always outperformed autonomous systems in drone racing. Now, a research group at the University of Zurich (UZH) has created an algorithm that can find the quickest trajectory to guide a quadrotor – a drone with four propellers – through a series of waypoints on a circuit.

To be useful, drones need to be quick. Because of their limited battery life, they must complete whatever task they have--searching for survivors on a disaster site, inspecting a building, delivering cargo--in the shortest possible time. And they may have to do it by going through a series of waypoints like windows, rooms, or specific locations to inspect, adopting the best trajectory and the right acceleration or deceleration at each segment. The best human drone pilots are very good at doing this and have so far always outperformed autonomous systems in drone racing. Now, a research group at the University of Zurich (UZH) has created an algorithm that can find the quickest trajectory to guide a quadrotor--a drone with four propellers--through a series of waypoints on a circuit.

As aerial pictures show the extent of destruction, our correspondent reports on the situation on the ground.

Turbine maintenance is an expensive, high-risk task. According to a recent analysis from the news website, wind farm owners are expected to spend more than $40 billion on operations and maintenance over a decade. Another recent study finds by using drone-based inspection instead of traditional rope-based inspection, you can reduce the operational costs by 70% and further decrease revenue lost due to downtime by up to 90%. This blog post will present how drones, machine learning (ML), and Internet of Things (IoT) can be utilized on the edge and the cloud to make turbine maintenance safer and more cost effective. First, we trained the machine learning model on the cloud to detect hazards on the turbine blades, including corrosion, wear, and icing.

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The adoption of industrial drone programs for businesses has risen dramatically over the past five years, and is predicted to continue that growth – increasing at a 66.8 percent compound annual growth rate over the next year. Industrial drones are being used in major industries like insurance, mining aggregates, and industrials, involving cutting-edge technologies (AI, machine learning and deep data analytics) to drastically reduce the time workers spend gathering and analyzing data, while increasing accuracy and positively impacting customer value. With drones being more accurate at data collection and their technology increasingly paired with advanced ML/AL algorithms, the benefits of Smart Inspections become applicable across multiple industries. All of these working together result in a growing field impacting industrial work and forever changing how these industries operate on a daily basis globally: Smart Inspections. If your business is still'in the business' of performing manual inspections such as building roofs in insurance or stockpile inspections in the mining and aggregate industry, luckily there is a better and much more efficient method: Smart Inspections.

Abstract-- This paper addresses task-allocation problems with uncertainty in situational awareness for distributed autonomous robots (DARs). The uncertainty propagation over a task-allocation process is done by using the Unscented transform that uses the Sigma-Point sampling mechanism. It has great potential to be employed for generic task-allocation schemes, in the sense that there is no need to modify an existing task-allocation method that has been developed without considering the uncertainty in the situational awareness. The proposed framework was tested in a simulated environment where the decision-maker needs to determine an optimal allocation of multiple locations assigned to multiple mobile flying robots whose locations come as random variables of known mean and covariance. The simulation result shows that the proposed stochastic task allocation approach generates an assignment with 30% less overall cost than the one without considering the uncertainty.