Aviation

The explosive growth in e-commerce, the increasing urgency of de-carbonization, the rapid advances in Unmanned Aircraft Vehicle (UAV) technology, and the continuous disruptive development of the gig economy create needs and opportunities for dramatic improvements in urban package delivery. E-commerce may greatly increase the demand for such deliveries, traditionally made by truck and van, as urban residents substitute purchases made over the Internet for those acquired in brick-and-mortar stores. To mitigate the congestion impact of truck and van traffic, as well as reduce costs and travel times, last-mile delivery should in many cases be shifted toward non-motorized modes (e.g., walkers and bicyclists) and UAVs. While there is literature on how to optimally configure urban delivery systems composed of these modes, most of the research in this area does not consider these problems in the context of urban congestion. In addition to the familiar issues of urban street congestion, in the future, we may also see congestion above the city from UAV traffic, as the use of these vehicles for urban package delivery and other purposes intensifies. In this project, the authors develop a suite of multimodal, congestion-sensitive strategies for urban delivery, by integrating traditional motorized vehicles, non-motorized modes, and UAVs. Building on prior research for modelling and managing urban road congestion as well as logistics studies for UAV route optimization and scheduling, the authors develop new models that combine UAVs with other modes for integrated and coordinated urban package delivery. The impacts of the new multimodal strategies on roadway operations and safety are evaluated.

Flocking with multiple unmanned aerial vehicles (UAVs) offers significant potential for diverse applications due to its enhanced maneuverability, improved efficiency, and increased robustness. Collision avoidance is a critical and challenging issue for distributed flocking control with a UAV fleet, especially in dynamic environments with varying numbers of non-cooperative intruders. However, existing reinforcement learning based methods mainly focus on flocking with collision avoidance tasks with static obstacles and a fixed number of UAVs. In this article, the authors propose a scalable multi-agent reinforcement learning based method to solve the distributed flocking with collision avoidance problem for a scalable fleet of fixed-wing UAVs in dynamic environments. Specifically, the authors cast this problem in a decentralized partially observable Markov decision process framework and propose a scalable multi-agent reinforcement learning algorithm called spatial-temporal attention multi-agent actor-critic (STAAC). In this algorithm, the authors design a spatial-temporal attention-based population-invariant network architecture to facilitate the representation learning of dynamic dimensional observations. By integrating the local spatial attention and global temporal attention mechanisms, STAAC is able to adapt to the changes in the scale of UAV fleets and the number of intruders. Finally, the authors empirically demonstrate the effectiveness, scalability, and adaptability of the proposed approach in numerical simulations and hardware-in-the-loop experiments.

The anticipated proliferation of Unmanned Aerial Vehicles (UAVs) in the airspace in the coming years has raised concerns about how to manage their flights to avoid collisions and crashes at various stages of flight. To this end, many Unmanned Aircraft Traffic Management systems (UTM) have been designed. These systems use various methods for managing UAV conflicts. Several surveys have reviewed conflict resolution methods for UAVs. However, to the best of the authors’ knowledge, there is no survey specifically addressing conflict detection and resolution methods in UTM, particularly those using AI-based methods. Therefore, this article serves as a comprehensive survey of all UAVs conflicts detection and resolution methods proposed in the literature and their use in the UTM systems. This survey classifies the methods into two categories: classical (non-learning) methods and learning-based methods. Classical methods typically rely on pre-defined algorithms or rules for UAVs to avoid collisions, whereas Artificial Intelligence-based methods, including Machine Learning (ML) and especially Reinforcement Learning (RL), enable UAVs to adapt to their environment, autonomously resolve conflicts, and exhibit intelligent behavior based on their experiences. It also presents their application in the conflict resolution service for UTMs. Additionally, the challenges and issues associated with each type of methods are discussed. This article can serve as a foundational resource for researchers in guiding their selection of methods for conflict resolution, particularly those relevant to UTM systems.

Unmanned Aerial Vehicles (UAVs) offer a potential real-time air pollution monitoring system with flexible, high-resolution spatial and temporal data acquisition over challenging terrains. Compared with traditional static monitoring systems, UAVs can be deployed close to pollution sources, enhancing the accuracy and coverage entailed in air quality monitoring. Their potential remains to be maximised due to the complexity of regulations and the lack of standard policy guidelines. This review paper critically discusses 94 research articles filtered from 224 papers to determine the role of UAVs in air quality monitoring compared with traditional methods. The review presents UAV capabilities, low-cost sensor (LCS) performance, and operational limitations over different emission sources, from road transport, landfills, and urbanisation to marine vessels, biomass burning, industries, mining, thermal power plants, regional background pollution sources and environmental radiation. The study also highlights the benefit of UAVs in reaching remote regions while realising the limitations of UAV propeller’s downwash effects and LCS reliability and calibration. This review paper aims to help researchers and decision-makers benefit from UAV’s potential and the LCS in air pollution monitoring by developing elaborate monitoring protocols in data-poor areas. This research seeks to motivate researchers to create more advanced systems that address current limitations by emphasising the UAV system's strengths and weaknesses.

The measurement of ultrasonic surface velocity in concrete and the ultrasonic Time Of Flight method for estimating the depth of surface opening cracks in concrete are important techniques for maintenance of constructions, which are currently performed manually. This paper demonstrates the possibility to automate these measurements by means of an Unmanned Aerial Vehicle (UAV) equipped with a robotic arm, avoiding the risks and costs related to the manual methods. The automated measurements are performed by a special end effector with closed-loop force control that maintains the flying UAV stable while the robotic arm contacts the concrete surface with two piezoelectric transducers for the ultrasonic tests. The system is successfully validated by performing ultrasound measurements from the flying UAV on a test specimen with artificial cracks and on the T9 Metsovo bridge in Greece during an on-field trial, demonstrating an error margin lower than 1 % on crack depth.