Rep. Michael McCaul, R-Texas, criticizes America's stance on the world stage at the hands of President Joe Biden in an exclusive interview on'Sunday Morning Futures.' White House national security adviser Jake Sullivan was pressed in an interview Sunday over an alleged civilian casualty – a father of 10 who was tending sheep – by U.S. forces in Syria. CNN's Jake Tapper asked Sullivan about the reportedly botched missile strike, which the Pentagon initially claimed was a successful assassination of a "senior Al Qaeda leader," but later backtracked and launched an investigation. Sullivan said he could not comment on the matter until the Pentagon's "full and thorough investigation" was complete – and instead touted President Biden's record on military accountability. "It was President Biden who stood up with Secretary Austin's guidelines for this administration to ensure there would be accountability and oversight of any potential civilian casualties from counterterrorism strikes," Sullivan said. "So far we do not have evidence to validate the claims being made in Syria. But I am going to withhold any judgment on what actually happened here until the Pentagon's investigation is complete."

With technology for drones far advanced, the next step is to ensure they can fly safely in cities. Image credit: CC0 via Unsplash The Spanish resort town of Benidorm is known for its sandy beaches with clear waters, a skyline dominated by towering hotels and tourists from northern Europe. But one day in February, it also served as a testing ground for European society's future with drones. Since the local economy depends on tourism during the summer, Benidorm is relatively empty in winter – and that's a plus when it comes to safety while testing unmanned aerial vehicles (UAVs). The tall buildings that dominate the skyline also stand in nicely for those of a big city. In sum, it's an ideal place to try out new drone technology.

In this work, we present an approach to supervisory reinforcement learning control for unmanned aerial vehicles (UAVs). UAVs are dynamic systems where control decisions in response to disturbances in the environment have to be made in the order of milliseconds. We formulate a supervisory control architecture that interleaves with extant embedded control and demonstrates robustness to environmental disturbances in the form of adverse wind conditions. We run case studies with a Tarot T-18 Octorotor to demonstrate the effectiveness of our approach and compare it against a classic cascade control architecture used in most vehicles. While the results show the performance difference is marginal for nominal operations, substantial performance improvement is obtained with the supervisory RL approach under unseen wind conditions.

We present a unified pipeline architecture for a real-time detection system on an embedded system for UAVs. Neural architectures have been the industry standard for computer vision. However, most existing works focus solely on concatenating deeper layers to achieve higher accuracy with run-time performance as the trade-off. This pipeline of networks can exploit the domain-specific knowledge on aerial pedestrian detection and activity recognition for the emerging UAV applications of autonomous surveying and activity reporting. In particular, our pipeline architectures operate in a time-sensitive manner, have high accuracy in detecting pedestrians from various aerial orientations, use a novel attention map for multi-activities recognition, and jointly refine its detection with temporal information. Numerically, we demonstrate our model's accuracy and fast inference speed on embedded systems. We empirically deployed our prototype hardware with full live feeds in a real-world open-field environment.

Russia unleashed another widespread missile and drone attack overnight on cities across Ukraine, including targeting the capital, Kyiv, for the 10th time this month, Ukrainian officials said on Friday. At least three cruise missiles and six attack drones managed to evade air defenses, according to Ukraine's Air Force. There was no immediate information on casualties or what was hit. The air force said in a statement that three cruise missiles and 16 Iranian-made Shahed-136 drones had been intercepted, and local officials in Lviv said that five of those were over their region, in western Ukraine. The attack drones came in "several waves" with short intervals in between, according to the city's military administration, which said all had been shot down.

Apogee Strong co-founder Tim Kennedy joined'Fox & Friends Weekend' to discuss the U.S. response and the importance of fatherhood in America. U.S. military officials are walking back a claim that a senior al Qaeda leader was killed in a recent drone strike in Syria, a senior U.S. defense official confirmed to Fox News. The story was first reported by the Washington Post. The family of 56-year-old Lotfi Hassan Misto identified him as the person killed by the American missile on May 3, according to the Post. U.S. Central Command, or CENTCOM, oversaw the operation and released a statement on the day of the strike saying it conducted a strike "targeting a senior Al Qaeda leader."

Robots with the ability to balance time against the thoroughness of search have the potential to provide time-critical assistance in applications such as search and rescue. Current advances in ergodic coverage-based search methods have enabled robots to completely explore and search an area in a fixed amount of time. However, optimizing time against the quality of autonomous ergodic search has yet to be demonstrated. In this paper, we investigate solutions to the time-optimal ergodic search problem for fast and adaptive robotic search and exploration. We pose the problem as a minimum time problem with an ergodic inequality constraint whose upper bound regulates and balances the granularity of search against time. Solutions to the problem are presented analytically using Pontryagin's conditions of optimality and demonstrated numerically through a direct transcription optimization approach. We show the efficacy of the approach in generating time-optimal ergodic search trajectories in simulation and with drone experiments in a cluttered environment. Obstacle avoidance is shown to be readily integrated into our formulation, and we perform ablation studies that investigate parameter dependence on optimized time and trajectory sensitivity for search.

Summer is just around the corner, and you're bound to want to chronicle your adventures. But struggling with a selfie or tracking down strangers is so 2010. The AIR NEO AI-Powered Autofly Camera Drone is the easiest, most fun way to take HD photos and videos of you and your friends completely hands-free. This intuitive camera drone offers multiple Autofly modes that track your motion using AI to get the perfect shot. In the Wide mode, it will take two wide-angle shots from five feet away to get the whole group in the frame.

The layout design of pipelines is a critical task in the construction industry. Currently, pipeline layout is designed manually by engineers, which is time-consuming and laborious. Automating and streamlining this process can reduce the burden on engineers and save time. In this paper, we propose a method for generating three-dimensional layout of pipelines based on deep reinforcement learning (DRL). Firstly, we abstract the geometric features of space to establish a training environment and define reward functions based on three constraints: pipeline length, elbow, and installation distance. Next, we collect data through interactions between the agent and the environment and train the DRL model. Finally, we use the well-trained DRL model to automatically design a single pipeline. Our results demonstrate that DRL models can complete the pipeline layout task in space in a much shorter time than traditional algorithms while ensuring high-quality layout outcomes.

This paper presents a generalized model for real-time detection of flying objects that can be used for transfer learning and further research, as well as a refined model that is ready for implementation. We achieve this by training our first generalized model on a data set containing 40 different classes of flying objects, forcing the model to extract abstract feature representations. We then perform transfer learning with these learned parameters on a data set more representative of real world environments (i.e., higher frequency of occlusion, small spatial sizes, rotations, etc.) to generate our refined model. Object detection of flying objects remains challenging due to large variance object spatial sizes/aspect ratios, rate of speed, occlusion, and clustered backgrounds. To address some of the presented challenges while simultaneously maximizing performance, we utilize the current state of the art single-shot detector, YOLOv8, in an attempt to find the best tradeoff between inference speed and mAP. While YOLOv8 is being regarded as the new state-of-the-art, an official paper has not been provided. Thus, we provide an in-depth explanation of the new architecture and functionality that YOLOv8 has adapted. Our final generalized model achieves an mAP50-95 of 0.685 and average inference speed on 1080p videos of 50 fps. Our final refined model maintains this inference speed and achieves an improved mAP50-95 of 0.835.