Digital Twin is an emerging technology at the forefront of Industry 4.0, with the ultimate goal of combining the physical space and the virtual space. To date, the Digital Twin concept has been applied in many engineering fields, providing useful insights in the areas of engineering design, manufacturing, automation, and construction industry. While the nexus of various technologies opens up new opportunities with Digital Twin, the technology requires a framework to integrate the different technologies, such as the Building Information Model used in the Building and Construction industry. In this work, an Information Fusion framework is proposed to seamlessly fuse heterogeneous components in a Digital Twin framework from the variety of technologies involved. This study aims to augment Digital Twin in buildings with the use of AI and 3D reconstruction empowered by unmanned aviation vehicles. We proposed a drone-based Digital Twin augmentation framework with reusable and customisable components. A proof of concept is also developed, and extensive evaluation is conducted for 3D reconstruction and applications of AI for defect detection.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. An unidentified inspector who failed to discover a crack in the Hernando de Soto Bridge linking Arkansas and Tennessee that prompted the span's closure was fired Monday morning and may face charges, according to reports. Arkansas Department of Transportation Director Lorie Tudor said the inspector was fired after drone video showed the crack on the bridge spanning the Mississippi River in May 2019. "This is unacceptable," Tudor said at a news conference.

German aviation outfit Volocopter has shown off another concept craft, this time aimed at capturing the commuter market. The VoloConnect is intended to transport up to four passengers over distances of up to 64 miles, taking people "from the city to […] suburban areas." In the release, the company says that the craft uses a hybrid lift and push design to electrically move bougie one percenters at speeds of up to 111 miles per hour. The VoloConnect is designed with six electrical motors and rotors, with a pair of propulsive fans jutting out behind. The use of the VTOL concept certainly, if this thing ever reaches the real world, would help it navigate inside cities while covering longer distances in open ground.

This article is part of the Policing and Technology Project, a collaboration between Future Tense and the Tech, Law, & Security Program at American University Washington College of Law that examines the relationship between law enforcement, police reform, and technology. There's a man pacing back and forth in the grocery store parking lot, evidently agitated, shouting at the sky. On the phone, a police dispatcher reassures you that someone is coming over to help--and so is a drone. Soon, you hear the telltale buzz of a drone overhead. Through its camera, someone is watching the agitated man in the parking lot, feeding information back to emergency services.

UAVs have found an important application in archaeological mapping. Majority of the existing methods employ an offline method to process the data collected from an archaeological site. They are time-consuming and computationally expensive. In this paper, we present a multi-UAV approach for faster mapping of archaeological sites. Employing a team of UAVs not only reduces the mapping time by distribution of coverage area, but also improves the map accuracy by exchange of information. Through extensive experiments in a realistic simulation (AirSim), we demonstrate the advantages of using a collaborative mapping approach. We then create the first 3D map of the Sadra Fort, a 15th Century Fort located in Gujarat, India using our proposed method. Additionally, we present two novel archaeological datasets recorded in both simulation and real-world to facilitate research on collaborative archaeological mapping. For the benefit of the community, we make the AirSim simulation environment, as well as the datasets publicly available.

FloodNet is a high-resolution image dataset acquired by a small UAV platform, DJI Mavic Pro quadcopters, after Hurricane Harvey. The dataset presents a unique challenge of advancing the damage assessment process for post-disaster scenarios using unlabeled and limited labeled dataset. We propose a solution to address their classification and semantic segmentation challenge. We approach this problem by generating pseudo labels for both classification and segmentation during training and slowly incrementing the amount by which the pseudo label loss affects the final loss. Using this semi-supervised method of training helped us improve our baseline supervised loss by a huge margin for classification, allowing the model to generalize and perform better on the validation and test splits of the dataset. In this paper, we compare and contrast the various methods and models for image classification and semantic segmentation on the FloodNet dataset.

More and more, drones are becoming a normal part of our future. "When we first introduced the integration of computer science with aerospace engineering to create self aware drones it seemed like an alien concept, but over the last couple of years A.I. has advanced exponentially while drone development has expanded to many conservation studies," says Princess Aliyah Pandolfi, Executive Director of Kashmir World Foundation (KwF). These flying robots aid workers with daily tasks, and innovation keeps pushing technology in a direction to further help. However, drones don't always have to help people, drones can also be used to safeguard wildlife. Conservationists are chronically underfunded and understaffed, so the use of drones can give much-needed assistance.

There is a growing interest in using electric vehicles (EVs) and drones for many applications. However, battery-oriented issues, including range anxiety and battery degradation, impede adoption. Battery swap stations are one alternative to reduce these concerns that allow the swap of depleted for full batteries in minutes. We consider the problem of deriving actions at a battery swap station when explicitly considering the uncertain arrival of swap demand, battery degradation, and replacement. We model the operations at a battery swap station using a finite horizon Markov Decision Process model for the stochastic scheduling, allocation, and inventory replenishment problem (SAIRP), which determines when and how many batteries are charged, discharged, and replaced over time. We present theoretical proofs for the monotonicity of the value function and monotone structure of an optimal policy for special SAIRP cases. Due to the curses of dimensionality, we develop a new monotone approximate dynamic programming (ADP) method, which intelligently initializes a value function approximation using regression. In computational tests, we demonstrate the superior performance of the new regression-based monotone ADP method as compared to exact methods and other monotone ADP methods. Further, with the tests, we deduce policy insights for drone swap stations.

The US Air Force conducted a flight test that paves the way for AI-piloted fighter jets to man the skies. The military group flew its Skyborg Autonomy Core System (ACS) for two hours and 10 minutes over Florida and Gulf of Mexico on April 29. The technology is a combination of hardware and software designed to act as a brain for a drone, allowing it to conduct operations without human interference. Fitted to a Kratos UTAP-22 tactical unmanned vehicle, the ACS demonstrated basic aviation capabilities and responded to navigational commands, while reacting to geo-fences, adhering to aircraft flight envelopes and demonstrating coordinated maneuvering. The US Air Force conducted a flight test that paves the way for AI-piloted fighter jets to man the skies.

Avoiding hybrid obstacles in unknown scenarios with an efficient flight strategy is a key challenge for unmanned aerial vehicle applications. In this paper, we introduce a technique to distinguish dynamic obstacles from static ones with only point cloud input. Then, a computationally efficient obstacle avoidance motion planning approach is proposed and it is in line with an improved relative velocity method. The approach is able to avoid both static obstacles and dynamic ones in the same framework. For static and dynamic obstacles, the collision check and motion constraints are different, and they are integrated into one framework efficiently. In addition, we present several techniques to improve the algorithm performance and deal with the time gap between different submodules. The proposed approach is implemented to run onboard in real-time and validated extensively in simulation and hardware tests. Our average single step calculating time is less than 20 ms.