In 2019, drone startups raised a record $1.2 billion in funding, according to Drone Industry Insights. It's a different story in 2020: Through mid-August, only about $240 million in disclosed capital has flowed into private companies, based on public data. It seems the investment side of the industry has hit a lull if not outright slump. Further evidence: Chinese drone manufacturer DJI, which owns more than 75% of the consumer drone market, has been shedding staff in a major reorganization for the last few months. That makes the $100 million mega-round picked up by Skydio in April all the more noteworthy.

In mid-July, a UPS subsidiary called Flight Forward and the drone company Matternet started a project with the Wake Forest Baptist Health system in North Carolina. The companies' aims are decidedly futuristic: to ferry specialty medicines and protective equipment between two of the system's facilities, less than a half-mile apart. Think of it: little flying machines, zipping about at speeds up to 43 mph, bearing the goods to heal. At this point, though, the drone operations are a little, well, human. The quadcopters must be operated by specialized drone pilots, who must pass a challenging aeronautical knowledge test to get their licenses.

On the photo: image processing based on samples taken by robotic platform in combination with artificial intelligence.

Recently, I had the pleasure of visiting one of the most innovative AI companies in Pittsburgh. Gather AI, an autonomous inventory management AI company, which is revolutionizing the warehousing industry as the title mentions "one drone at a time." Gather AI's founding team is made up of three graduates from Carnegie Mellon University's Robotics Institute, which includes co-founder and chief robotics officer Sankalp Arora, co-founder, and chief technology officer Daniel Maturana, and co-founder and chief security officer, Geetesh Dubey. Gather AI uses state of the art robotics, classic AI methods, and a deep learning engine to enable autonomous inventory monitoring and management, which uses a fleet of drones. Each drone is fully autonomous and paired to a tablet device, which provides inventory data.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A forward-operating, satellite-networked Air Force drone comes across a small, moving group of enemy surface ships heading toward vulnerable areas, when instant data is sent to Navy ships' commanders and land-based Army weapons operators in real-time, enabling a coordinated, multi-pronged attack using deck-fired Tomahawk missiles fired from the ocean, land-based attack rockets and fighter jets armed with air-to-surface weapons. This possible scenario, in which land, sea and air warriors and weapons system share information in real-time across vast, otherwise dispersed areas to optimize attack is precisely what the Pentagon intends with its new doctrinal and technical approach to future war. The Army, Navy and Air Force each have secure information-sharing combat network technology programs.

Bezos said in the 2013 interview that it would take four or five years to have those drone deliveries. It turns out that using remote-controlled aerial gizmos to drop stuff at our homes is incredibly difficult, prone to risk and potentially more trouble than it's worth. Like driverless cars, drone technology in populated areas is more complicated than most people expected, and it has been -- mostly for good reason -- tightly controlled in the United States by government agencies worried about drones straying into the path of airplanes, dropping out of the sky onto our heads or unwittingly spying through people's windows. It wasn't until this week that the F.A.A. gave Amazon permission to do drone deliveries. And drones might never be practical for deliveries when someone in a vehicle could do the same thing in a fraction of the time and cost. Drones are a great public relations jolt for Amazon, but let's not put too much stock in them for awhile -- maybe ever.

Drone technology continues to advance, as more research and development is targeted toward traffic control systems for the small, flying devices. The Nevada Institute for Autonomous Systems (NIAS) was recently awarded the bulk of a roughly $1.8 million earmark by the Federal Aviation Administration (FAA) to study and test virtual unmanned traffic management technology, known as UTM. The effort is a partnership among NIAS, an FAA-designated drone test site; Switch, maker of data-center technology; and ANRA Technologies, which produces drones. Switch and ANRA will lead demonstrations and testing of unmanned flight systems, while NIAS will explore some of the system and requirements to operate drone fleets safely. Advances in drone traffic control could not be more timely, say makers of the devices, as companies explore using drones for any number of on-demand deliveries -- from groceries to chicken wings.

Geese, swans and ducks are easily startled by drones, scientists have found. The flying machines are becoming increasingly common in the UK as the technology becomes cheaper and the devices easier to fly. However, conservationists from the British Trust for Ornithology (BTO) found the presence of the whirring machines can trigger fear in the birds, causing them to flee. Disturbances caused by drones could affect rare and protected species, causing them to waste energy and reduce time spent in winter feeding grounds, experts fear. Conservationists from the British Trust for Ornithology (BTO) found the presence of drones can trigger fear in waterbirds, causing them to flee.

Currently available quadrotor simulators have a rigid and highly-specialized structure: either are they really fast, physically accurate, or photo-realistic. In this work, we propose a paradigm-shift in the development of simulators: moving the trade-off between accuracy and speed from the developers to the end-users. We use this design idea to develop a novel modular quadrotor simulator: Flightmare. Flightmare is composed of two main components: a configurable rendering engine built on Unity and a flexible physics engine for dynamics simulation. Those two components are totally decoupled and can run independently from each other. This makes our simulator extremely fast: rendering achieves speeds of up to 230 Hz, while physics simulation of up to 200,000 Hz. In addition, Flightmare comes with several desirable features: (i) a large multi-modal sensor suite, including an interface to extract the 3D point-cloud of the scene; (ii) an API for reinforcement learning which can simulate hundreds of quadrotors in parallel; and (iii) an integration with a virtual-reality headset for interaction with the simulated environment. We demonstrate the flexibility of Flightmare by using it for two completely different robotic tasks: learning a sensorimotor control policy for a quadrotor and path-planning in a complex 3D environment.

A benchmark for multi-UAV task assignment is presented in order to evaluate different algorithms. An extended Team Orienteering Problem is modeled for a kind of multi-UAV task assignment problem. Three intelligent algorithms, i.e., Genetic Algorithm, Ant Colony Optimization and Particle Swarm Optimization are implemented to solve the problem. A series of experiments with different settings are conducted to evaluate three algorithms. The modeled problem and the evaluation results constitute a benchmark, which can be used to evaluate other algorithms used for multi-UAV task assignment problems.