On a cloudy Christmas morning last year, a rocket carrying the most powerful space telescope ever built blasted off from a launchpad in French Guiana. After reaching its destination in space about a month later, the James Webb Space Telescope (JWST) began sending back sparkling presents to humanity--jaw-dropping images that are revealing our universe in stunning new ways. Every year since 1988, Popular Science has highlighted the innovations that make living on Earth even a tiny bit better. And this year--our 35th--has been remarkable, thanks to the successful deployment of the JWST, which earned our highest honor as the Innovation of the Year. But it's just one item out of the 100 stellar technological accomplishments our editors have selected to recognize. The list below represents months of research, testing, discussion, and debate. It celebrates exciting inventions that are improving our lives in ways both big and small. These technologies and discoveries are teaching us about the ...

Artificial intelligence (AI) is often presented in binary terms in both popular culture and political analysis. Either it represents the key to a futuristic utopia defined by the integration of human intelligence and technological prowess, or it is the first step toward a dystopian rise of machines. This same binary thinking is practiced by academics, entrepreneurs, and even activists in relation to the application of AI in combating climate change. The technology industry's singular focus on AI's role in creating a new technological utopia obscures the ways that AI can exacerbate environmental degradation, often in ways that directly harm marginalized populations. In order to utilize AI in fighting climate change in a way that both embraces its technological promise and acknowledges its heavy energy use, the technology companies leading the AI charge need to explore solutions to the environmental impacts of AI.

Coco, the robot based delivery service, announced the official launch of COCO 1, a larger, more advanced version of its signature pink bot. The COCO 1 is a first of its kind delivery robot designed and manufactured in partnership with the largest micro mobility hardware manufacturer, Segway. Coco is currently deploying 1,000s of COCO 1 robots to serve local merchants in multiple cities, over the next few months. With its increased carrying capacity, the COCO 1 will deliver larger orders for a wider range of merchants, further eliminating the need for car-based delivery. Compared to the current model, the COCO 1 offers a number of added features including a more efficient drivetrain and a larger battery capacity that allows for an increased delivery radius of up to three miles, nearly double the radius of the original model.

The TriRhenaTech alliance presents the accepted papers of the 'Upper-Rhine Artificial Intelligence Symposium' held on October 27th 2021 in Kaiserslautern, Germany. Topics of the conference are applications of Artificial Intellgence in life sciences, intelligent systems, industry 4.0, mobility and others. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, Offenburg and Trier, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

This paper investigates an unmanned aerial vehicle (UAV)-assisted wireless powered mobile-edge computing (MEC) system, where the UAV powers the mobile terminals by wireless power transfer (WPT) and provides computation service for them. We aim to maximize the computation rate of terminals while ensuring fairness among them. Considering the random trajectories of mobile terminals, we propose a soft actor-critic (SAC)-based UAV trajectory planning and resource allocation (SAC-TR) algorithm, which combines off-policy and maximum entropy reinforcement learning to promote the convergence of the algorithm. We design the reward as a heterogeneous function of computation rate, fairness, and reaching of destination. Simulation results show that SAC-TR can quickly adapt to varying network environments and outperform representative benchmarks in a variety of situations.

Save $110: The Ninja Dragons Blade X 4K Drone is on sale for $89 as of Aug. 21 -- a more than 50% dip from its original price of $199. But the real nerding out comes when you take those cool shots yourself. The Ninja Dragons Blade X Drone offers a perfect entry-level setup for beginner pilots. A secondary camera and built-in stabilization system are present to boost the quality of your footage. Fly and shoot for over 10 minutes on a single charge. The Blade X ships with the gear needed to keep your device flying pristinely over time: backup blades, protective blade frames, a rechargeable battery, and charging cable, remote control, and clutch bag.

Infrastructure around the world is being linked together via sensors, machine learning and analytics. We examine the rise of the digital twin, the new leaders in industrial IoT (IIoT) and case studies that highlight the lessons learned from production IIoT deployments. Schneider Electric will launch a robo-advisor approach to climate change and sustainability with the help of machine learning, artificial intelligence and data science. The company, best known for its EcoStruxure platform for the industrial sector as well as data centers, will offer AI-assisted advising to its energy and sustainability services. Schneider Electric is emerging as a critical edge computing player.

Matt Carlson is the Vice President of Business Development at WiBotic Inc, a company that provides reliable wireless power solutions to charge aerial, mobile and aquatic robot systems. Why are wireless charging solutions so important to the future of robotics? Robots need the ability to autonomously charge for most applications. It simply isn't cost effective to hire a staff of workers to manage battery charging or battery swapping. However, most autonomous charging today is done using docking stations that require physical mating of electrical contacts.

Tesla's battery research partner has released a new paper on a battery cell that could last over 1 million miles, which they say is going to be particularly useful in'robot taxis' -- something that Tesla wants to bring to market. When talking about the economics of Tesla's future fleet of robotaxis at the Tesla Autonomy Event, Tesla CEO Elon Musk emphasized that the vehicles need to be durable in order for the economics to work: The cars currently built are all designed for a million miles of operation. The drive unit is design, tested, and validated for 1 million miles of operation. But the CEO admitted that the battery packs are not built to last 1 million miles. Earlier this year, Musk said that they built Model 3 to last as long as a commercial truck, a million miles, and the battery modules should last between 300,000 miles and 500,000 miles.

The battery is a key component of autonomous robots. Its performance limits the robot's safety and reliability. Unlike liquid-fuel, a battery, as a chemical device, exhibits complicated features, including (i) capacity fade over successive recharges and (ii) increasing discharge rate as the state of charge (SOC) goes down for a given power demand. Existing formal verification studies of autonomous robots, when considering energy constraints, formalise the energy component in a generic manner such that the battery features are overlooked. In this paper, we model an unmanned aerial vehicle (UA V) inspection mission on a wind farm and via probabilistic model checking in PRISM show (i) how the battery features may affect the verification results significantly in practical cases; and (ii) how the battery features, together with dynamic environments and battery safety strategies, jointly affect the verification results. Potential solutions to explicitly integrate battery prognostics and health management (PHM) with formal verification of autonomous robots are also discussed to motivate future work. Keywords: Formal verification · Probabilistic model checking · PRISM · Autonomous systems · Unmanned aerial vehicle · Battery PHM. 1 Introduction Autonomous robots, such as unmanned aerial vehicles (UA V) (commonly termed drones 3), unmanned underwater vehicles (UUV), self-driving cars and legged-robots, obtain increasingly widespread applications in many domains [14].