With the advancement of deep learning methods it is imperative that autonomous systems will increasingly become intelligent with the inclusion of advanced machine learning algorithms to execute a variety of autonomous operations. One such task involves the design and evaluation for a subsystem of the perception system for object detection and tracking. The challenge in the creation of software to solve the task is in discovering the need for a dataset, annotation of the dataset, selection of features, integration and refinement of existing algorithms, while evaluating performance metrics through training and testing. This research effort focuses on the development of a machine learning pipeline emphasizing the inclusion of assurance methods with increasing automation. In the process, a new dataset was created by collecting videos of moving object such as Roomba vacuum cleaner, emulating search and rescue (SAR) for indoor environment. Individual frames were extracted from the videos and labeled using a combination of manual and automated techniques. This annotated dataset was refined for accuracy by initially training it on YOLOv4. After the refinement of the dataset it was trained on a second YOLOv4 and a Mask R-CNN model, which is deployed on a Parrot Mambo drone to perform real-time object detection and tracking. Experimental results demonstrate the effectiveness of the models in accurately detecting and tracking the Roomba across multiple trials, achieving an average loss of 0.1942 and 96% accuracy.

A remaining challenge in multirotor drone flight is the autonomous identification of viable landing sites in unstructured environments. One approach to solve this problem is to create lightweight, appearance-based terrain classifiers that can segment a drone's RGB images into safe and unsafe regions. However, such classifiers require data sets of images and masks that can be prohibitively expensive to create. We propose a pipeline to automatically generate synthetic data sets to train these classifiers, leveraging modern drones' ability to survey terrain automatically and the ability to automatically calculate landing safety masks from terrain models derived from such surveys. We then train a U-Net on the synthetic data set, test it on real-world data for validation, and demonstrate it on our drone platform in real-time.

Numerous remarkable advancements have been made in accuracy, speed, and parallelism for solving the Unmanned Aerial Vehicle Route Planing (UAVRP). However, existing UAVRP solvers face challenges when attempting to scale effectively and efficiently for larger instances. In this paper, we present a generalization framework that enables current UAVRP solvers to robustly extend their capabilities to larger instances, accommodating up to 10,000 points, using widely recognized test sets. The UAVRP under a large number of patrol points is a typical large-scale TSP problem.Our proposed framework comprises three distinct steps. Firstly, we employ Delaunay triangulation to extract subgraphs from large instances while preserving global features. Secondly, we utilize an embedded TSP solver to obtain sub-results, followed by graph fusion. Finally, we implement a decoding strategy customizable to the user's requirements, resulting in high-quality solutions, complemented by a warming-up process for the heatmap. To demonstrate the flexibility of our approach, we integrate two representative TSP solvers into our framework and conduct a comprehensive comparative analysis against existing algorithms using large TSP benchmark datasets. The results unequivocally demonstrate that our framework efficiently scales existing TSP solvers to handle large instances and consistently outperforms state-of-the-art (SOTA) methods. Furthermore, since our proposed framework does not necessitate additional training or fine-tuning, we believe that its generality can significantly advance research on end-to-end UAVRP solvers, enabling the application of a broader range of methods to real-world scenarios.

The police chief of a small Nebraska city has come forward with a warning for New Jersey after his community was terrorized by mystery drones. Ord, Nebraska Police Chief Chris Grooms revealed to DailyMail.com Across nearly three weeks of nighttime encounters, typically between 7pm and 11pm, these inexplicable SUV-sized drones operated'with impunity,' Chief Grooms said, and sometimes seemed to be'toying with law enforcement.' 'A lot of reports by ranchers stated that these objects were harassing their horses or cattle on a nightly basis,' he added. Some of the drones reached speeds of 120mph.

Sen. Tim Kaine, D-Va., tells Fox News Digital he's frustrated by U.S. officials not being forthcoming about the drone incursions over Langley Air Force Base. Nearly one year after mysterious drones hovered near a top-secret military base in Virginia for 17 days, Sen. Tim Kaine says he is "very frustrated" with "so many unanswered questions" that remain. The Virginia Democrat said his state delegation will get a classified briefing on the situation Thursday. For more than two weeks in December 2023, the mystery drones flew into restricted airspace over the installation, home to key national security sites and the F-22 Raptor stealth fighters. The Pentagon has said little about the incidents other than to confirm they occurred after a Wall Street Journal report in October.

Kristina Cooper, vice president at Travelmation in Florida, is sharing her smart tips and tricks to avoid frustrating flight delays and cancellations when traveling this holiday season. As Americans brace for holiday travel, flyers might be thinking about whether the uptick in drone sightings will affect take-off and landing. Drone sightings across the northeast skies have been reported with some spotted near or over airport spaces. The Transportation Security Administration (TSA) expects nearly 40 million people to fly over the holidays, according to the agency's website. Sightings have been reported near airports in Connecticut, Massachusetts, New Jersey and New York, according to several airports in those states that confirmed the sightings to Fox News Digital.

A recent influx of videos supposedly showing "drones" or other spooky unidentified aerial phenomena flying over darkened US skylines appears to be the result, in part, of AI-trickery. Since late November, residents in New Jersey and at least five other states have reported spotting bright objects flying overhead. The sightings have stirred speculation, amplified by celebrities, commentators, and prominent public officials, that this is nefarious, experimental technology. Now, several of the viral videos surfacing on TikTok and X over the past week are capitalizing on the panic; they also appear to exhibit the hallmark calling cards of generative AI manipulation. Almost none of the videos reviewed by Popular Science had any official label or disclosure from social media platforms warning users about possible digital editing.

Mayor Michael Melham of Belleville, New Jersey, gives an update on the mysterious drone sightings across the Garden State on'The Faulkner Focus.' Sen. Rand Paul, R-Ky., blocked a Senate bill Wednesday that would have authorized resources for state and local authorities to track drones that have mystified residents across New Jersey and the Northeast in recent weeks. Paul objected to the passage of the bill, citing his long-standing concerns over expanding governmental powers. "This body must not rush to grant sweeping surveillance powers without proper consideration and debate by the committees of jurisdiction," he said. Sen. Rand Paul, ranking member of the Senate Homeland Security and Governmental Affairs Committee, blocked a bill Wednesday that would have allowed local law enforcement agencies to track aerial drones.

U.S. Army Staff Sergeant Payton May joins'Fox & Friends Weekend' and sheds light on being reunited with his former military service dog Yyacob. The Senate voted to pass the 895 billion annual defense policy bill that includes a pay raise for U.S. servicemembers and a provision that restricts transgender care. The bill passed 85 to 14, and now heads to President Biden's desk for his signature. The legislation scored a more bipartisan vote in the Senate than it did in the House, where more Democrats voted no on the legislation in protest of the transgender provisions. The bill prohibits military health care provider Tricare from paying for transgender care "that could result in sterilization" for children under 18.

'Fox & Friends First' host Carley Shimkus discusses the Fox Flight Team joining the search for UAPs in the Northeast and a classified briefing for lawmakers stating nothing'nefarious' is happening in New Jersey skies. Congress is set to pass legislation to avert a government shutdown that will reauthorize the government's ability to intercept and track unauthorized drones and crack down on U.S. investment in China. The 1,500 page continuing resolution (CR), which will fund the government until March 14, includes a provision reauthorizing a Department of Homeland Security program allowing agencies to coordinate and counter threats from drones. That authority, passed in 2018, was set to expire Friday – at a time when concerns about drone incursions are at an all-time high. However, it is a simple reauthorization of a program many drone experts say is outdated.