There were also suggestions on social media that had such an experiment taken place, it was more likely to have been a pre-planned scenario rather than the AI-enabled drone being powered by machine learning during the task - which basically means it would not have been choosing its own outcomes as it went along, based on what had happened previously.

AGI, while powerful, could have negative consequences, warned Diveplane CEO Mike Capps and Liberty Blockchain CCO Christopher Alexander. Legislation moving through the House would provide millions of dollars for research on how to incorporate artificial intelligence into drone technology in an effort to keep the U.S. ahead of China in this increasingly important component of national security. The House Committee on Science, Space, and Technology last week approved legislation from committee Chairman Frank Lucas, R-Okla., that he says needs to pass before China becomes locked in as the world's major supplier of drones. His bill, the National Drone and Advanced Air Mobility Research and Development Act, would fund about $1.6 billion in research over the next five years to give a boost to U.S.-based drone manufacturers. "To say China has cornered this market is an understatement," Lucas said last week. "One single company with extensive ties to the Chinese Communist Party and the People's Liberation Army produces 80% of the drones used recreationally in the U.S." A staff member works on an unmanned aerial vehicle at Guizhou University in Guiyang, China, on May 23, 2023.

This paper presents a wireless data collection framework that employs an unmanned aerial vehicle (UAV) to efficiently gather data from distributed IoT sensors deployed in a large area. Our approach takes into account the non-zero communication ranges of the sensors to optimize the flight path of the UAV, resulting in a variation of the Traveling Salesman Problem (TSP). We prove mathematically that the optimal waypoints for this TSP-variant problem are restricted to the boundaries of the sensor communication ranges, greatly reducing the solution space. Building on this finding, we develop a low-complexity UAV-assisted sensor data collection algorithm, and demonstrate its effectiveness in a selected use case where we minimize the total energy consumption of the UAV and sensors by jointly optimizing the UAV's travel distance and the sensors' communication ranges.

The ability to converse with humans and follow natural language commands is crucial for intelligent unmanned aerial vehicles (a.k.a. drones). It can relieve people's burden of holding a controller all the time, allow multitasking, and make drone control more accessible for people with disabilities or with their hands occupied. To this end, we introduce Aerial Vision-and-Dialog Navigation (AVDN), to navigate a drone via natural language conversation. We build a drone simulator with a continuous photorealistic environment and collect a new AVDN dataset of over 3k recorded navigation trajectories with asynchronous human-human dialogs between commanders and followers. The commander provides initial navigation instruction and further guidance by request, while the follower navigates the drone in the simulator and asks questions when needed. During data collection, followers' attention on the drone's visual observation is also recorded. Based on the AVDN dataset, we study the tasks of aerial navigation from (full) dialog history and propose an effective Human Attention Aided Transformer model (HAA-Transformer), which learns to predict both navigation waypoints and human attention.

We study the problem of visual surface inspection of a bridge for defects using an Unmanned Aerial Vehicle (UAV). We do not assume that the geometric model of the bridge is known beforehand. Our planner, termed GATSBI, plans a path in a receding horizon fashion to inspect all points on the surface of the bridge. The input to GATSBI consists of a 3D occupancy map created online with LiDAR scans. Occupied voxels corresponding to the bridge in this map are semantically segmented and used to create a bridge-only occupancy map. Inspecting a bridge voxel requires the UAV to take images from a desired viewing angle and distance. We then create a Generalized Traveling Salesperson Problem (GTSP) instance to cluster candidate viewpoints for inspecting the bridge voxels and use an off-the-shelf GTSP solver to find the optimal path for the given instance. As the algorithm sees more parts of the environment over time, it replans the path to inspect novel parts of the bridge while avoiding obstacles. We evaluate the performance of our algorithm through high-fidelity simulations conducted in AirSim and real-world experiments. We compare the performance of GATSBI with a classical exploration algorithm. Our evaluation reveals that targeting the inspection to only the segmented bridge voxels and planning carefully using a GTSP solver leads to a more efficient and thorough inspection than the baseline algorithm.

One of the motivations for exploring flapping-wing aerial robotic systems is to seek energy reduction, by maintaining manoeuvrability, compared to conventional unmanned aerial systems. A Flapping Wing Flying Robot (FWFR) can glide in favourable wind conditions, decreasing energy consumption significantly. In addition, it is also necessary to investigate the power consumption of the components in the flapping-wing robot. In this work, two sets of the FWFR components are analyzed in terms of power consumption: a) motor/electronics components and b) a vision system for monitoring the environment during the flight. A measurement device is used to record the power utilization of the motors in the launching and ascending phases of the flight and also in cruising flight around the desired height. Additionally, an analysis of event cameras and stereo vision systems in terms of energy consumption has been performed. The results provide a first step towards decreasing battery usage and, consequently, providing additional flight time.

The Gulf of Mexico has around 50 species of sharks, with around 20 to 30 species that beachgoers and fishermen can encounter. A shark was captured on drone footage Monday circling a man and a child swimming at a popular beach in Alabama. The footage, taken by 15-year-old Jackson Silvio and obtained by Fox News Digital, shows the man and child wading further out into the water at Orange Beach. At one point the shark appeared to swim just within a few feet of the man. The shark can be seen following them, swimming in a circle as it gets closer.

A General Motors pickup has never hauled something like this. GM Defense is collaborating with military contractor Black Sage Technologies to integrate a drone defense system into the Infantry Squad Vehicle (ISV) that GM Defense recently began supplying to the US Army. The ISV is based on the last-generation Chevrolet Colorado ZR2 midsize pickup and manufactured in Concord, N.C., using frames supplied by NASCAR's Hendrick Motorsports. The midsize truck was engineered for high-speed off-road driving and designed to fit inside a CH-47 Chinook helicopter, slung from a UH-60 Blackhawk helicopter, or air-dropped from a cargo plane by parachute for quick deployment into the field. The vehicle can be outfitted to fit nine troops, but there are several configurations that mix passenger, cargo and arms carrying capabilities.

Senior foreign affairs correspondent Greg Palkot reports the latest from London. Russian President Vladimir Putin is speaking out following a drone attack on Moscow, calling the strikes an attempt by Ukraine to "intimidate" his country. The remarks come after eight drones targeted Russia's capital early Tuesday before being shot down or diverted with electronic jammers. Moscow Mayor Sergei Sobyanin said the attack caused "insignificant damage" to several buildings and that two people received treatment for unspecified injuries but did not need hospitalization. Residents of two high-rise buildings damaged in the attack were evacuated.

The United States is encouraging Ukraine to launch cross-border "terrorist" attacks, a Russian official alleged, after Moscow was hit by a series of drone strikes. The White House, meanwhile, said on Tuesday it did not support attacks inside Russia, and that it is still gathering information on the incident. "What are these attempts to hide behind the phrase they are'gathering information'?" Anatoly Antonov, Russia's ambassador to the US, said in remarks published on the Telegram messaging channel. "This is an encouragement for Ukrainian terrorists."