In this paper, we propose an autonomous UAV path planning framework using deep reinforcement learning approach. The objective is to employ a self-trained UAV as a flying mobile unit to reach spatially distributed moving or static targets in a given three dimensional urban area. In this approach, a Deep Deterministic Policy Gradient (DDPG) with continuous action space is designed to train the UAV to navigate through or over the obstacles to reach its assigned target. A customized reward function is developed to minimize the distance separating the UAV and its destination while penalizing collisions. Numerical simulations investigate the behavior of the UAV in learning the environment and autonomously determining trajectories for different selected scenarios.

This week, nearly every major company developing autonomous vehicles in the U.S. halted testing in an effort to stem the spread of COVID-19, which has sickened more than 250,000 people and killed over 10,000 around the world. Still some experts argue pandemics like COVID-19 should hasten the adoption of driverless vehicles for passenger pickup, transportation of goods, and more. Autonomous vehicles still require disinfection -- which companies like Alphabet's Waymo and KiwiBot are conducting manually with sanitation teams -- but in some cases, self-driving cars and delivery robots might minimize the risk of spreading disease. In a climate of social distancing, when on-demand services from Instacart to GrubHub have taken steps to minimize human contact, one factor in driverless cars' favor is that they don't require a potentially sick person behind the wheel. Tellingly, on Monday, when Waymo grounded its commercial robotaxis with human safety drivers, it initially said it would continue to operate the driverless autonomous cars in its fleet.

Drones can do many things, but avoiding obstacles is not their strongest suit yet – especially when they move quickly. Although many flying robots are equipped with cameras that can detect obstacles, it typically takes from 20 to 40 milliseconds for the drone to process the image and react. It may seem quick, but it is not enough to avoid a bird or another drone, or even a static obstacle when the drone itself is flying at high speed. This can be a problem when drones are used in unpredictable environments, or when there are many of them flying in the same area. Reaction of a few milliseconds In order to solve this problem, researchers at the University of Zurich have equipped a quadcopter (a drone with four propellers) with special cameras and algorithms that reduced its reaction time down to a few milliseconds – enough to avoid a ball thrown at it from a short distance.

Police in Spain have turned to drones to encourage people to stay indoors and practice social distancing during the country's now surging COVID-19 outbreak. The drones have been equipped with speakers that officers can use to broadcast live messages from their squad cars. The drones are part of neighborhood sweeps police have been implementing to enforce a country-wide lockdown that began on Saturday. The drones have been used in Madrid to help clear parks and other public spaces where many in the country had continued to gather in spite of growing health concerns, according to a report in Popular Mechanics. Under the country's lockdown, which was implemented the same day Prime Minister Pedro Sánchez's wife Begoña Gómez tested positive for COVID-19, people are banned from leaving home for any reason other than to buy essential supplies and medicine or to go to work. As with many other countries around the world, Spain has required schools and all non-essential businesses to close, including museums, sporting events, and restaurants, which are restricted to delivery and takeout orders.

Experts have got together to discuss the future of home automation and reveal their predictions for the future of home automation. According to futurologists, around 90 per cent of household chores will be automated thanks to robots, drones and AI by 2040. These will be carried out by drones, robots and virtual AI butlers that will help with laundry, dusting and even making the bed, they claim. Kings College Professor Mischa Dohler and futurologist Dr Ian Pearson created a report with consumer site comparethemarket.com to predict how homes will look in two decades time. Experts have got together to discuss the future of home automation and revealed their predictions for the future of home automation.

Roads in the United Kingdom are set to see a host of new transport technology in the near future as the nation's government greenlights a series of real-world tests. The blueprint it creates could set precedent for other countries that want to implement new mobility tech.

After a passenger infected with the novel coronavirus boarded the Diamond Princess cruise ship in January, the virus quickly spread, eventually infecting at least 712 and killing seven. Critics labeled the ship quarantined in Yokohama a floating petri dish, and at least one Japanese expert attributed the explosion of cases to food trays passed out by infected crew. Could robots have made a difference? As countries around the world grapple with COVID-19, front line medical workers are deploying robots, telemedicine and other technologies to help contain the pandemic. China and Spain have used drones to monitor people during lockdown campaigns, while South Korea has deployed them to help disinfect areas in Daegu, an epidemic hotspot.

Learning to control robots without requiring models has been a long-term goal, promising diverse and novel applications. Yet, reinforcement learning has only achieved limited impact on real-time robot control due to its high demand of real-world interactions. In this work, by leveraging a learnt probabilistic model of drone dynamics, we achieve human-like quadrotor control through model-based reinforcement learning. No prior knowledge of the flight dynamics is assumed; instead, a sequential latent variable model, used generatively and as an online filter, is learnt from raw sensory input. The controller and value function are optimised entirely by propagating stochastic analytic gradients through generated latent trajectories. We show that "learning to fly" can be achieved with less than 30 minutes of experience with a single drone, and can be deployed solely using onboard computational resources and sensors, on a self-built drone.

From the initially reported outbreak of coronavirus (COVID-19) in China to the spread of it across the globe, Medtech companies are rolling out robots and drones to help fight it and provide services and care to those quarantined or practicing social distancing. This pandemic has fast-tracked the "testing" of robots and drones in public as officials seek out the most expedient and safe way to grapple with the outbreak and limit contamination and spread of the virus. As one of the world's most influential tech innovators and a country that had prioritized the advancement of robotics as a key component in its Made in China 2025 initiative, when COVID-19 broke out in China it became an ideal time to see what robots and drones could do to support humans in battling the virus. Here are some of the ways robots and drones are being used to fight COVID-19. COVID-19 is taxing healthcare systems and medical professionals in every country it spreads to.

Due to the years of military experience achieved by our owner and the history he has in hybrid electric design and development, the move towards cargo drone UAV's was a natural choice. Initial testing started with 10kg payloads back in 2008 and we have provided fully functional designs for several operators for private testing. As we expanded our range we have designed and tested platforms with payloads up to 150kg but we are now developing the design of a 300kg payload version.