Google will not allow its artificial intelligence (AI) software to be used in weapons or unreasonable surveillance efforts under new standards for its business decisions in the nascent field, the Alphabet unit said on Thursday. The restriction could help Google management defuse months of protest by thousands of employees against the company's work with the U.S. military to identify objects in drone video. Google instead will seek government contracts in areas such as cybersecurity, military recruitment and search and rescue, CEO Sundar Pichai said in a blog post. "We want to be clear that while we are not developing AI for use in weapons, we will continue our work with governments and the military in many other areas," he said. Breakthroughs in the cost and performance of advanced computers have carried AI from research labs into industries such as defence and health in the last couple of years.

A team of researchers at the University of Luxembourg and the University of Ontario Institute of Technology have recently proposed a new approach to design trajectories for energy-efficient unmanned aerial vehicle (UAV)-enabled wireless communications. Their paper, prepublished on arXiv, specifically focuses on cases in which an UAV acts as a flying base station (BS) to serve ground users (GSs) within some predetermined latency constraints. "Our goal is to design the UAV trajectory to minimize the total energy consumption while satisfying the RT requirement and energy budget, which is accomplished via jointly optimizing the trajectory and UAV's velocities along subsequent hops," the researchers wrote in their paper. Optimizing a UAV's trajectory and its velocities together can be somewhat difficult to achieve. To do so, the researchers developed an approach that carries out two consecutive steps. Their approach entails the use of two distinct algorithms, a heuristic search and a dynamic programming (DP) algorithm.

This past week, the Interior Department ordered the grounding of its drone fleet that was made in China or contained Chinese parts. This comes on the heels of similar actions taken by the Department of Homeland Security in May and the United States Army in 2017. While political pundits credit the ban to the Trump administration's policy initiatives against the Asian superpower, many cybersecurity analysts cite legitimate security concerns. As a result, there is a bipartisan bill pending, The American Security Drone Act of 2019, to ban all Federal agencies from using any Chinese-made aerial vehicles. As Senator Richard Blumenthal explains, "Like it or not, drones are our future. Without Congressional action, adversaries like China and Iran will use drone technology as tiny Trojan Horses to spy on our government, our critical infrastructure – even our hospitals and homes. This bill will ensure that we don't send China and others a gold-plated, flying invitation to steal our intellectual property, undermine our domestic technology, and spy on our communities."

It packs everything critical to be a quality drone. It has a good camera, good range, and a good controller. It holds up well in the wind and is quick enough to be fun. And it's so small that you're more likely to throw it in your bag and take it on Instagram adventures. The small size is the Mavic Mini's main selling point.

Police Scotland has unveiled a new aerial drone system to help in searches for missing and vulnerable people. The remotely-piloted aircraft system (RPAS) can see things we can't to try to work out where people are. It uses advanced cameras and neural computer networks to spot someone it is looking for - from "a speck" up to 150 metres away. Its recognition software is compact enough to be run on a phone, with the technology learning as it goes. "The drone itself has very special sensors on it," said Insp Nicholas Whyte, of Police Scotland's air support unit.

A glossy high rise in the heart of Miami aims to be the first residential building in the U.S. with a specially designed rooftop to accommodate a Jetsons-like future where cars take to the skies. Halfway through the construction of Paramount Miami World Center, developers determined that the $4 billion, 60-story complex needed something extra to stand out among the vast array of living options for the super-rich. So they installed an observation deck at the top that doubles as a landing pad for vertical takeoff and landing vehicles, often called VTOLs, or flying cars. The tower will have its grand opening early in 2020. Meanwhile, a flying car's reality, where passengers can be dropped off at home like Amazon drone packages, could be decades away – if ever.

Greenhouses, search-and-rescue teams and warehouses are all looking for new methods to enable surveillance in a manner that is quick and safe for the objects and people surrounding them. Many of them already found their way into robotics, but wheeled ground-bound systems have limited maneuverability. Ideally it would be great if flying robots, a.k.a. A group or a swarm of them should be ideal to cover as much ground as possible. The price of fully autonomous MAVs usually comes with the cost of weight and size.

Unmanned Aerial Vehicles (UAVs), autonomously-guided aircraft, are widely used for tasks involving surveillance and reconnaissance. A version of the pursuit-evasion problems centered around UAVs and its variants has been extensively studied in recent years due to numerous breakthroughs in AI. We present an approach to UAV pursuit-evasion in a 2D aerial-engagement environment using reinforcement learning (RL), a machine learning paradigm concerned with goal-oriented algorithms. In this work, a UAV wielding the greedy shooter strategy engages with a UAV trained using deep Q-learning techniques. Simulated results show that the latter UAV wins every engagement in which the UAVs are suffciently separated during initialization. This approach highlights an exhaustive and robust application of reinforcement learning to pursuit-evasion that provides insight into effective strategies for UAV flight and interaction.

Delivery drones could hitch a ride by landing on public transport to deliver to far away locations. Most prototype delivery drones can currently travel around 12 miles to make a delivery before their battery fails. This range could be extended by about four-and-half times if the agile machines landed on the roofs of buses or trams to piggyback in the right direction, finds study. Shushman Choudhury, who led the research at Stanford University in California told The New Scientist: 'We already have this existing, generally decent infrastructure for most good cities and we're just benefiting from that. 'You could now service deliveries over a city while having far fewer depots.'

Here is a list of white papers. Please let us know if there is a white paper you would like to see that's not on the list. Just send us an email containing details about the white paper including Name, Publication Date, Contact Telephone, Email and URL if available. This white paper addresses the most difficult challenges facing manufacturers and OEMs as they compete to reach their production targets. With their equipment running at maximum loads, how can they avoid breakdowns in pneumatic components?