As wildfires have grown in numbers and intensity throughout the western United States, it's caused a run on new kinds of technology that can help fight them. That includes machine learning for data analysis, drones, unmanned aerial vehicles, and satellite surveillance. California alone tracked 4.2 million acres burned in 2020, with five of the six largest fires in state history occurring simultaneously. That has led to multiple tech-driven firefighting solutions being approved in the state, including predictive analysis, fire-spotting from orbit, and AI-powered equipment inspections. "AI-enabled systems are already being used to coordinate disaster relief, conduct reconnaissance, and direct recovery efforts. Detecting patterns, trends, and anomalies in supply chains and for logistical support has also become a common task for Machine Learning algorithms," said JT Kostman, the CEO of artificial intelligence firm ProtectedBy.AI, in an interview with Lifewire.

Quadrotors are among the most agile flying robots. However, planning time-optimal trajectories at the actuation limit through multiple waypoints remains an open problem. This is crucial for applications such as inspection, delivery, search and rescue, and drone racing. Early works used polynomial trajectory formulations, which do not exploit the full actuator potential because of their inherent smoothness. Recent works resorted to numerical optimization but require waypoints to be allocated as costs or constraints at specific discrete times. However, this time allocation is a priori unknown and renders previous works incapable of producing truly time-optimal trajectories. To generate truly time-optimal trajectories, we propose a solution to the time allocation problem while exploiting the full quadrotor's actuator potential. We achieve this by introducing a formulation of progress along the trajectory, which enables the simultaneous optimization of the time allocation and the trajectory itself. We compare our method against related approaches and validate it in real-world flights in one of the world's largest motion-capture systems, where we outperform human expert drone pilots in a drone-racing task.

The nature of war continues to evolve through the 21st century with conflict zones shifting from jungles and deserts to coastal cities. Not to mention the rapidly increasing commercial availability of cutting-edge technologies including UAVs and wireless communications. To help the Marine Corps best prepare for these increased complexities and challenges, the Department of Defense tasked DARPA with developing a digital training and operations planning tool. The result is the Prototype Resilient Operations Testbed for Expeditionary Urban Scenarios (PROTEUS) system, a real-time strategy simulator for urban-littoral warfare. When the PROTEUS program first began in 2017, "there was a big push across DARPA under what we call a sustainment focus area, and that included urban warfare," Dr. Tim Grayson, director of DARPA's Strategic Technology Office, told Engadget, looking at how to best support and "sustain" US fighting forces in various combat situations until they can finish their mission.

Israel's defense minister warned Thursday that his country is prepared to strike Iran, issuing the threat against the Islamic Republic after a fatal drone strike on a oil tanker at sea that his nation blamed on Tehran. The comments by Benny Gantz come as Israel lobbies countries for action at the United Nations over last week's attack on the oil tanker Mercer Street that killed two people. The tanker, struck off Oman in the Arabian Sea, is managed by a firm owned by an Israeli billionaire. The U.S. and the United Kingdom also blamed Iran for the attack, but no country has offered evidence or intelligence to support the claim. Iran, which along with its regional militia allies has launched similar drone attacks, has denied being involved.

The US Navy is developing a pilotless solar-powered plane that can fly for 90 days at a time to help keep a watchful eye on naval ships below or act as a communications relay platform. The plane, dubbed'Skydweller' and developed by Skydweller Aero, builds on the manned Solar Impulse 2 aircraft that flew around the world in 2015 and 2016, but had to stop every five days. The upgraded version will eliminate the cockpit, allowing space for hardware that allows for autonomous abilities. Skydweller Aero CEO Robert Miller told New Scientist: 'When we remove the cockpit, we are enabling true persistence and providing the opportunity to install up to about 400 kilograms of payload capacity.' The pilotless craft will feature 236-foot long wings that are blanked in solar cells, but its makers may add hydrogen fuel cells for an additional boost.

The United States said Wednesday it suspected Iranian involvement in the alleged hijacking of a ship in the Gulf of Oman as it vowed to work with Britain to respond to an earlier deadly attack it blamed on Tehran. Oman said that the Asphalt Princess, an asphalt and bitumen tanker, was involved in "a hijacking incident in international waters" and that it deployed aircraft and naval ships. The United States and Britain said that the murky incident in the Gulf of Oman concluded after one day, with the alleged hijackers leaving the Panamanian-flagged vessel. "We believe that these personnel were Iranian, but we're not in a position to confirm this at this time," State Department spokesman Ned Price told reporters in Washington. "Iran has undertaken a pattern of belligerence in terms of proxy attacks in the region and of course, these maritime attacks," Price said, while adding that circumstances in the latest incident were "still emerging".

We enjoyed messing around with the games, but pretty quickly our mind wandered to what these things can do. VR is still a young space, and you quite frequently find yourself wanting an app that doesn't exist. At the top of our list: Could you view the world through a drone's camera while you flew it? We'd taken a few AI courses in the past, and we thought that single camera 3D VR might just be possible. In the world of autonomous vehicles, there is similar work underway in the form of research into "Pseudo-LIDAR".

Due to changes in model dynamics or unexpected disturbances, an autonomous robotic system may experience unforeseen challenges during real-world operations which may affect its safety and intended behavior: in particular actuator and system failures and external disturbances are among the most common causes of degraded mode of operation. To deal with this problem, in this work, we present a meta-learning-based approach to improve the trajectory tracking performance of an unmanned aerial vehicle (UAV) under actuator faults and disturbances which have not been previously experienced. Our approach leverages meta-learning to train a model that is easily adaptable at runtime to make accurate predictions about the system's future state. A runtime monitoring and validation technique is proposed to decide when the system needs to adapt its model by considering a data pruning procedure for efficient learning. Finally, the reference trajectory is adapted based on future predictions by borrowing feedback control logic to make the system track the original and desired path without needing to access the system's controller. The proposed framework is applied and validated in both simulations and experiments on a faulty UAV navigation case study demonstrating a drastic increase in tracking performance.

In this paper, we address the problem of adaptive path planning for accurate semantic segmentation of terrain using unmanned aerial vehicles (UAVs). The usage of UAVs for terrain monitoring and remote sensing is rapidly gaining momentum due to their high mobility, low cost, and flexible deployment. However, a key challenge is planning missions to maximize the value of acquired data in large environments given flight time limitations. To address this, we propose an online planning algorithm which adapts the UAV paths to obtain high-resolution semantic segmentations necessary in areas on the terrain with fine details as they are detected in incoming images. This enables us to perform close inspections at low altitudes only where required, without wasting energy on exhaustive mapping at maximum resolution. A key feature of our approach is a new accuracy model for deep learning-based architectures that captures the relationship between UAV altitude and semantic segmentation accuracy. We evaluate our approach on the application of crop/weed segmentation in precision agriculture using real-world field data.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Troubling drone footage emerged online Sunday that reportedly showed up to 1,000 migrants being held by border patrol in Mission, Texas, prompted criticism from Republicans who said the footage underscores the crisis at the border. "An absolute catastrophe from Joe Biden, Kamala Harris, and House & Senate Democrats," Rep. Elise Stefanik, R-N.Y., posted on Twitter. He said the footage showed the "largest group of migrants we've ever seen being held by Border Patrol under Anzalduas Bridge in Mission, TX." "Looks like it could be up to 1,000 people," he posted.