With "social distancing" the phrase of the week around the world, the time may have come for communities and governments to embrace drone delivery – not just for speed and convenience, but to protect themselves and vulnerable members. The World Economic Forum has published a blog post highlighting the contributions that drones have made in the fight against COVID-19. "3 Ways China is Using Drones to Fight Coronavirus" brings out the contributions made by drone delivery in an environment where limiting human-to-human contact is of paramount importance. In China, drone delivery company JD worked with government stakeholders to dramatically increase service areas to bring supplies to quarantined and isolated areas. "With the support of the local government, e-commerce company JD deployed its drone team. That team quickly conducted ground surveys, designed flight corridors, requested airspace access permission and conducted final flight tests. In just a few days, several drone delivery corridors were put in place replacing hours-long drives with a 2 km flight that could be completed in just 10 minutes," says the World Economic Forum's article.

When a precision-guided artillery projectile exploded an enemy target from 64km (39.8 miles) away in the Arizona desert during a recent live-fire exercise, the Army took a new step toward redefining land-attack tactics and paving the way toward a new warfare era in long-range fires. In a March 2020 demonstration firing of the emerging Long Range Precision Fires program at Yuma Proving Grounds, Ariz., an Army Howitzer blasted an Excalibur 155m artillery round out to ranges twice that of what existing artillery weapons are now capable of. The new weapon in development, called Extended Range Cannon Artillery, not only preserves the GPS-guided precision attack options characteristic of present-day artillery, but also extends attack ranges from roughly 30km (18.6 miles) out to nearly 70km (43.5 miles). This, senior Army weapons developers explain, gives ground artillery commanders the ability to destroy previously unreachable air and ground targets. "This provides a longer range capability, enabling commanders to attack helicopters, UAVs and go after other new targets farther range," Gen.

The civil aviation authority is working with industry, health officials and security services to put these policies into place. The CAAC unmanned aerial system office leadership stated, "Drones are playing key roles in managing the COVID-19 outbreak... It proves that lessons learnt from real world practices are critical for developing a sound regulatory framework whereby the potential of drone technology can be realized." As the world continues to tackle this crisis, these lessons can reshape how we protect and care for people during health emergencies.

A novel deep multi-agent reinforcement learning framework is proposed to identify and resolve conflicts among a variable number of aircraft in a high-density, stochastic, and dynamic sector in en route airspace. Currently the sector capacity is limited by human air traffic controller's cognitive limitation. In order to scale up to a high-density airspace, in this work we investigate the feasibility of a new concept (autonomous separation assurance) and a new approach (multi-agent reinforcement learning) to push the sector capacity above human cognitive limitation. We propose the concept of using distributed vehicle autonomy to ensure separation, instead of a centralized sector air traffic controller. Our proposed framework utilizes an actor-critic model, Proximal Policy Optimization (PPO) that we customize to incorporate an attention network. By using the attention network, we are able to encode the information from a variable number of intruder aircraft into a fixed length vector and allow the agents to learn which intruder aircraft's information is critical to achieve the optimal performance. This allows the agents to have access to variable aircraft information in the sector in a scalable, efficient approach to achieve high traffic throughput under uncertainty. The agents are trained using a centralized learning, decentralized execution scheme where one neural network is learned and shared by all agents in the environment. To validate the proposed framework, we designed three challenging case studies in the BlueSky air traffic control environment. Numerical results show the proposed framework significantly reduces the offline training time without sacrificing performance.

Autonomous Things (AuT) technology has not only found use cases in several industries including retail, security, transportation, military, and more – it's already transforming the very way we live. Find everything you should know about AuT in this guide. Autonomous Things (AuT), also known as the Internet of Autonomous Things (IoAT), are devices that use machine learning and artificial intelligence (AI) algorithms to complete specific tasks. AuTs are equipped with sensors, AI and analytical capabilities to improve the things they can do. To that effect, each machine can make its own decision and complete tasks autonomously. Some examples of AuT are self-driving cars, drones, autonomous smart home devices, and every other technology that does not need human control to be operational.

In real life, providing security for a set of large areas by covering the area with Unmanned Aerial Vehicles (UAVs) is a difficult problem that consist of multiple objectives. These difficulties are even greater if the area coverage must continue throughout a specific time window. We address this by considering a Vehicle Routing Problem with Time Windows (VRPTW) variation in which capacity of agents is one and each customer (target area) must be supplied with more than one vehicles simultaneously without violating time windows. In this problem, our aim is to find a way to cover all areas with the necessary number of UAVs during the time windows, minimize the total distance traveled, and provide a fast solution by satisfying the additional constraint that each agent has limited fuel. We present a novel algorithm that relies on clustering the target areas according to their time windows, and then incrementally generating transportation problems with each cluster and the ready UAVs. Then we solve transportation problems with the simplex algorithm to generate the solution. The performance of the proposed algorithm and other implemented algorithms to compare the solution quality is evaluated on example scenarios with practical problem sizes.

WASHINGTON – The United States waged a series of precision airstrikes on Thursday against an Iran-backed militia in Iraq that it blamed for a major rocket attack a day earlier that killed two American troops and a 26-year-old British soldier. The U.S. strikes appeared limited in scope and narrowly tailored, targeting five weapons storage facilities used by Kataib Hezbollah militants -- including facilities used to store weaponry for past attacks on U.S.-led coalition troops, the Pentagon said. Iraq's military said in a statement that the U.S. airstrikes hit four locations in Iraq. The U.S. military did not estimate how many people in Iraq may have been killed in the strikes, which officials said were carried out by piloted aircraft. But there no was no indication of the kind of high-profile killings that President Donald Trump authorized in January, when the United States targeted a top Iranian general, Qassem Soleimani.

The American retaliation led to a siege of the United States Embassy in Baghdad and then an American drone attack that killed the leader of Iran's elite Quds force, Maj. The cycle of attacks and counterattacks ended more than two weeks later after Iran launched 16 cruise missiles at bases in Iraq that house American forces. No one was killed by the Iranian missile attacks and tensions had appeared to subside. An Iraqi military official said that hours after the attack on Wednesday, the American-led coalition responded with airstrikes on camps used by Kataib Hezbollah near Abu Kamal in Syria, just across the border from Qaim, Iraq. However American officials said the United States had not carried out those strikes.

ZipRecruiter, the Santa Monica based employment marketplace named One of the World's Most Innovative Companies in 2019 by Fast Company just released their much-anticipated Future of Work Report 2020. The Artificial Intelligence (AI) jobs gold rush is spreading to more states. This is good news for the future of work. Jobs that require AI, machine learning, robotics, and engineering skills will continue to dominate as AI-enabled systems replace manual labor. In this article, I will walk you through some of the highlights.

An NTHU team has developed an AI chip that follows the streamlined function of a fruit fly optic nerve. A major limitation for aerial drones is the tradeoff between weight and battery capacity, which limits their range and usefulness for applications such as agriculture and infrastructure inspection. To address this challenge, a multidisciplinary team at National Tsing Hua University in Hsinchu, Taiwan, has developed an artificial intelligence processor that mimics the optical nerves of a fruit fly. This AI chip enables unmanned aerial vehicles (UAVs) to automatically avoid obstacles while staying in an "ultra-power-saving mode," said the researchers. The team was led by professors Tang Kea-tiong of the Department of Electrical Engineering and Lo Chung-chuan of the Department of Life Sciences at National Tsing Hua University (NTHU).