Safe autonomous navigation is an essential and challenging problem for robots operating in highly unstructured or completely unknown environments. Under these conditions, not only robotic systems must deal with limited localisation information, but also their manoeuvrability is constrained by their dynamics and often suffer from uncertainty. In order to cope with these constraints, this manuscript proposes an uncertainty-based framework for mapping and planning feasible motions online with probabilistic safety-guarantees. The proposed approach deals with the motion, probabilistic safety, and online computation constraints by: (i) incrementally mapping the surroundings to build an uncertainty-aware representation of the environment, and (ii) iteratively (re)planning trajectories to goal that are kinodynamically feasible and probabilistically safe through a multi-layered sampling-based planner in the belief space. In-depth empirical analyses illustrate some important properties of this approach, namely, (a) the multi-layered planning strategy enables rapid exploration of the high-dimensional belief space while preserving asymptotic optimality and completeness guarantees, and (b) the proposed routine for probabilistic collision checking results in tighter probability bounds in comparison to other uncertainty-aware planners in the literature. Furthermore, real-world in-water experimental evaluation on a non-holonomic torpedo-shaped autonomous underwater vehicle and simulated trials in the Stairwell scenario of the DARPA Subterranean Challenge 2019 on a quadrotor unmanned aerial vehicle demonstrate the efficacy of the method as well as its suitability for systems with limited on-board computational power.

In this paper, the design of an optimal trajectory for an energy-constrained drone operating in dynamic network environments is studied. In the considered model, a drone base station (DBS) is dispatched to provide uplink connectivity to ground users whose demand is dynamic and unpredictable. In this case, the DBS's trajectory must be adaptively adjusted to satisfy the dynamic user access requests. To this end, a meta-learning algorithm is proposed in order to adapt the DBS's trajectory when it encounters novel environments, by tuning a reinforcement learning (RL) solution. The meta-learning algorithm provides a solution that adapts the DBS in novel environments quickly based on limited former experiences. The meta-tuned RL is shown to yield a faster convergence to the optimal coverage in unseen environments with a considerably low computation complexity, compared to the baseline policy gradient algorithm. Simulation results show that, the proposed meta-learning solution yields a 25% improvement in the convergence speed, and about 10% improvement in the DBS' communication performance, compared to a baseline policy gradient algorithm. Meanwhile, the probability that the DBS serves over 50% of user requests increases about 27%, compared to the baseline policy gradient algorithm.

China's leading agri-tech company XAG has just launched a patented granule spreading system JetSeed at its Special Event held in Ruoergai Grassland, Aba, Sichuan. JetSeed Granule Spreading System is designed to dispense granules such as seeds, fertilisers and pesticides precisely and effectively to any environment through high-speed airflows. It helps to combat grassland degradation, one of the world's biggest environmental challenges, using AI prescription map and high-accuracy drone spreading solution. At the launch event, XAG introduced this cutting-edge technology to Ruoergai Grassland, one of China's most primitive nature reserves, by spreading grass seeds on 670 hectares of degraded land with a fleet of P30 Plant Protection UASs configured with JetSeed Granule Spreading System. This is the first time that drones, AI and airflow seeding technologies were harnessed to restore the grassland biomes at plateau area.

If you enjoyed this video, be sure to check out other videos on my channel: https://www.youtube.com/channel/UCSY4... Thanks for watching. Take care and stay safe.

After co-writing the best-selling adult novel of 2018, Bill Clinton and James Patterson have teamed up for another political thriller. "The President's Daughter" will be released in June 2021, the book's publishers announced Thursday. As with the million-selling "The President Is Missing," the new novel will be a rare joint release by rival companies: Alfred A. Knopf, which has released Clinton's "My Life" among other works, and Little, Brown and Co., Patterson's longtime publisher. "I never imagined I'd be writing a book with a master storyteller like Jim, much less two," Clinton said in a statement. "I was grateful for the success of the first book, and I believe readers will enjoy reading'The President's Daughter' as much as I'm enjoying working on it."

You've heard the buzzword at CES, drone conferences, on forums and in tech company marketing. But what is 5G, and what does it actually mean for the drone industry? In human-speak, 5G means significantly faster networks than the current standard, 4G. For example, 5G is expected to allow you to download an entire movie to your phone within seconds, while it could take many, many times that to download a movie over 4G. In the drone industry, that means not just the ability to quickly transfer massive files from a mapping project.

This article is part of Privacy in the Pandemic, a Future Tense series. Since the pandemic began, authorities in New Delhi, Italy, Oman, Connecticut, and China have begun to experiment with fever-finding drones as a means of mass COVID-19 screening. They're claiming the aircraft can be used to better understand the health of the population at large and even to identify potentially sick individuals, who can then be pulled aside for further diagnostic testing. In Italy, police forces are reportedly using drones to read the temperatures of people who are out and about during quarantine, while officials in India are hoping to use thermal-scanner-equipped drones to search for "temperature anomalies" in people on the ground. A Lithuanian drone pilot even used a thermal-scanning drone to read the temperature of a sick friend who didn't own a thermometer. Unfortunately, there's almost no evidence that these fever-detecting drones actually work.

High throughput genetic analysis is a tool that allows researchers to analyze a lot of DNA data in a short period of time. The work is commonly done in a lab with scientific instruments. Larry Purcell uses it to evaluate thousands of agricultural test plots at once. He does it from a distance of 100 feet -- straight up. Using an off-the-shelf aerial drone, Purcell can identify those soybean plants that have the genetic make-up, or genotype, for high rates of nitrogen fixation.

The US Air Force (USAF) has launched a competition to design the artificially intelligent software, called Skyborg, that would control its planned fleet of loyal wingman unmanned air vehicles (UAV). The service intends to grant indefinite delivery/indefinite quantity contracts worth $400 million per awardee to develop the software and related hardware, it says in a request for proposals released on 15 May. The USAF is looking for technical and cost proposals from companies by 15 June 2020 and intends to award multiple companies contracts, though it may award just one contract or no contracts, based on proposals. Skyborg would be artificially intelligent software used to control the flight path, weapons and sensors of large numbers of UAVs. Automating flight control, in particular via artificial intelligence, is seen as necessary to allow a single person, perhaps a backseat pilot in a fighter aircraft, to command multiple UAVs at once.

Collision detection is one of the most challenging tasks for Unmanned Aerial Vehicles (UAVs), especially for small or micro UAVs with limited computational power. In nature, fly insects with compact and simple visual systems demonstrate the amazing ability to navigating and avoid collision in a complex environment. A good example of this is locusts. Locusts avoid collision in a dense swarm relying on an identified vision neuron called Lobula Giant Movement Detector (LGMD) which has been modelled and applied on ground robots and vehicles. LGMD as a fly insect's visual neuron, is an ideal model for UAV collision detection. However, the existing models are inadequate in coping with complex visual challenges unique for UAVs. In this paper, we proposed a new LGMD model for flying robots considering distributed spatial-temporal computing for both excitation and inhibition to enhance the looming selectivity in flying scenes. The proposed model integrated recent discovered presynaptic connection types in biological LGMD neuron into a spatial-temporal filter with linear distributed interconnection. Systematic experiments containing quadcopter's first person view (FPV) flight videos demonstrated that the proposed distributed presynaptic structure can dramatically enhance LGMD's looming selectivity especially in complex flying UAV applications.