The rapid advancements in unmanned aerial vehicles (UAVs) have unlocked numerous applications, including environmental monitoring, disaster response, and agricultural surveying. Enhancing the collective behavior of multiple decentralized UAVs can significantly improve these applications through more efficient and coordinated operations. In this study, we explore a Recurrent PPO model for target localization in perceptually degraded environments like places without GNSS/GPS signals. We first developed a single-drone approach for target identification, followed by a decentralized two-drone model. Our approach can utilize two types of sensors on the UAVs, a detection sensor and a target signal sensor. The single-drone model achieved an accuracy of 93%, while the two-drone model achieved an accuracy of 86%, with the latter requiring fewer average steps to locate the target. This demonstrates the potential of our method in UAV swarms, offering efficient and effective localization of radiant targets in complex environmental conditions.

Event cameras provide low-latency perception for only milliwatts of power. This makes them highly suitable for resource-restricted, agile robots such as small flying drones. Self-supervised learning based on contrast maximization holds great potential for event-based robot vision, as it foregoes the need to high-frequency ground truth and allows for online learning in the robot's operational environment. However, online, onboard learning raises the major challenge of achieving sufficient computational efficiency for real-time learning, while maintaining competitive visual perception performance. In this work, we improve the time and memory efficiency of the contrast maximization learning pipeline. Benchmarking experiments show that the proposed pipeline achieves competitive results with the state of the art on the task of depth estimation from events. Furthermore, we demonstrate the usability of the learned depth for obstacle avoidance through real-world flight experiments. Finally, we compare the performance of different combinations of pre-training and fine-tuning of the depth estimation networks, showing that on-board domain adaptation is feasible given a few minutes of flight.

Small unmanned aerial vehicles (UAVs) have become standard tools in reconnaissance and surveying for both civilian and defense applications. In the future, UAVs will likely play a pivotal role in autonomous package delivery, but current multi-rotor candidates suffer from poor energy efficiency leading to insufficient endurance and range. In order to reduce the power demands of package delivery UAVs while still maintaining necessary hovering capabilities, companies like Amazon are experimenting with hybrid Vertical Take-Off and Landing (VTOL) platforms. Tailsitter VTOLs offer a mechanically simple and cost-effective solution compared to other hybrid VTOL configurations, and while advances in hardware and microelectronics have optimized the tailsitter for package delivery, the software behind its operation has largely remained a critical barrier to industry adoption. Tailsitters currently lack a generic, computationally efficient method of control that can provide strong safety and robustness guarantees over the entire flight domain. Further, tailsitters lack a closed-form method of designing dynamically feasible transition maneuvers between hover and cruise. In this paper, we survey the modeling and control methods currently implemented on small-scale tailsitter UAVs, and attempt to leverage a nonlinear dynamic model to design physically realizable, continuous-pitch transition maneuvers at constant altitude. Primary results from this paper isolate potential barriers to constant-altitude transition, and a novel approach to bypassing these barriers is proposed. While initial results are unsuccessful at providing feasible transition, this work acts as a stepping stone for future efforts to design new transition maneuvers that are safe, robust, and computationally efficient.

Fox News correspondent Nate Foy breaks down what witnesses are saying about the drones flying over New Jersey on'Your World.' Officials in New Jersey say they're taking mystery drone sightings, now reported in 10 counties across the state, "seriously," with the suspicious aircraft recently confirmed to have been spotted near the state's largest reservoir. The reason for the drones' presence near the Round Valley Reservoir in Hunterdon County, near the Garden State's border with Pennsylvania, is unclear, according to NJ.com. Similarly unclear are any potential connections to other drones spotted in the recent onslaught of suspicious activity that's taken the state by storm, the outlet continues. The drone sighting near the reservoir wasn't the only recent one in Hunterdon County – another was reported near its 911 Center in Flemington. "There have been reports of single drones hovering over people's houses for hours at a time," Hunterdon County Commissioner John Lanza noted at a Tuesday board meeting.

New footage of multiple eerie'triangle' craft flying above New Jersey has only compounded the mystery for locals. At least five or possibly six of the unidentified drones were captured in the new, 50-second cell phone video, which one commenter declared was'the clearest video yet.' One drone, heard roaring in the skies as it moved through the darkness, appeared to have a cluster of white lights on its underbelly and red lights blinking at the tips of its wings and tail. Another drone came into frame that resembled a classic'black triangle' UFO or the triangular TR-3B, which beamed bright white lights from its nose, wingtips and tail. Since mid-November, a wave of unexplained drone sightings above central Jersey has left both law enforcement and the general public watching the skies, hunting for clues on what these mysterious night flights might be.

Helicopters and drones have been used to try and trace the location of the three climbers, who set out to climb Mt Cook on Saturday. Ms Walker said drone footage showed evidence of where the climbers had begun to cross the slopes below the Zurbriggen Ridge. This included footprints and equipment, including clothes and energy gels, which are thought to have belonged to the men. Scaling Mt Cook via the Zurbriggen Ridge is a Grade Four climb, according to New Zealand alpine group Climb NZ. This mean that it requires "sound mountaineering judgement and experience". Both Blair and Romero are said to have been experienced climbers.

Officials are still investigating unusual drone activity that has been reported in recent weeks in New Jersey. The FAA set temporary restrictions above Trump National Golf Club in Bedminster in response. New Jersey Gov. Phil Murphy said he spoke with state and federal officials about unusual drone activity in parts of the region, including the vicinity of President-elect Trump's Bedminster golf club, but stressed there was no threat to public safety. In a Thursday post on X, Murphy said he convened a briefing with Homeland Security Secretary Alejandro Mayorkas, senior officials from DHS, the state police and state Homeland Security and Preparedness, as well as New Jersey's congressional delegation. "We are actively monitoring the situation and in close coordination with our federal and law enforcement partners on this matter," he wrote.

Officials are still investigating unusual drone activity that has been reported in recent weeks in New Jersey. The FAA set temporary restrictions above Trump National Golf Club in Bedminster in response. Authorities investigating the unusual drone activity observed several times in northern New Jersey in recent days, including the vicinity of President-elect Trump's Bedminster golf club, continue to stress that there is no threat to public safety. Multiple videos show drones flying in Somerset and Morris counties over the past few weeks, including Dec. 1 and Dec. 3. In a video from Nov. 25, a Morris County resident named Mike Walsh spotted drones flying over Black River Middle School in Chester.

Officials are still investigating unusual drone activity reported in recent weeks in New Jersey. The FAA set temporary restrictions over Trump National Golf Club in Bedminster. Authorities are still investigating mysterious drone activity observed several times in northern New Jersey in recent days, including the vicinity of President-elect Trump's Bedminster golf club. Multiple videos show drones flying in Somerset and Morris counties over the past few weeks, including Dec. 1 and Dec. 3. In a video from Nov. 25, a Morris County resident named Mike Walsh spotted drones flying over Black River Middle School in Chester.

The inspection of wind turbine blades (WTBs) is crucial for ensuring their structural integrity and operational efficiency. Traditional inspection methods can be dangerous and inefficient, prompting the use of unmanned aerial vehicles (UAVs) that access hard-to-reach areas and capture high-resolution imagery. In this study, we address the challenge of enhancing defect detection on WTBs by integrating thermal and RGB images obtained from UAVs. We propose a multispectral image composition method that combines thermal and RGB imagery through spatial coordinate transformation, key point detection, binary descriptor creation, and weighted image overlay. Using a benchmark dataset of WTB images annotated for defects, we evaluated several state-of-the-art object detection models. Our results show that composite images significantly improve defect detection efficiency. Specifically, the YOLOv8 model's accuracy increased from 91% to 95%, precision from 89% to 94%, recall from 85% to 92%, and F1-score from 87% to 93%. The number of false positives decreased from 6 to 3, and missed defects reduced from 5 to 2. These findings demonstrate that integrating thermal and RGB imagery enhances defect detection on WTBs, contributing to improved maintenance and reliability.