This paper, intended for expert and non-expert audiences, evaluates the technical and regulatory requirements for Unmanned Aerial Systems (UAS) to operate beyond visual line of sight (BVLOS) services. UAS BVLOS operations have the potential to unlock value for the industry. However, the regulatory requirements and process can be complex and challenging for UAS operators. The work explored the BVLOS regulatory regime in the UK, Europe and the US and found similarities in process and requirements covering themes like Detect and Avoid (DAA), Remote identification and Reliable Connectivity. A unifying goal across these jurisdictions is to operate BVLOS safely and securely in non-segregated airspace. However, operating BVLOS in segregated airspace as the default or routine mode could accelerate approval and adoption. The paper reviewed existing challenges, highlighting Coverage, Capacity and Redundancy as critical for UAS BVLOS Operations. The work also highlighted the crucial role of Non-terrestrial Network (NTN) assets like Satellites and HAPS (High Altitude Platform Station) since terrestrial networks (not optimised for aerial platform coverage) may not be reliable for BVLOS connectivity.
Wildfires have proven to be a significantly exigent issue over the past decades. An increasing amount of research has recently been focused on the use of Unmanned Aerial Vehicles (UAVs) and multi-UAV systems for wildfire monitoring. This work focuses on the development of a decentralized framework for the purpose of monitoring active wildfires and their surrounding areas with fixed wing UAVs. It proposes a distributed fire data update methodology, a new formation algorithm based on virtual forces, fine-tuned by a Genetic Algorithm (GA), to arrange virtual agents into the monitoring area, and a control strategy to safely and efficiently guide fixed wing UAVs to loiter over the structured virtual agents. The system is tested in Software In The Loop (SITL) simulation with up to eight UAVs. The simulation results demonstrate the effectiveness of the system in monitoring the fire in a persistent manner and providing updated situational awareness data. The experiments show that the proposed framework is able to achieve and maintain coverage up to 100% over the area of interest, and very accurate fire representation. However, the performance is decreased for the experiments with low UAV numbers and large fire sizes.
This paper presents, an autonomous and scalable monitoring system for early detection and spread estimation of wildfires by leveraging low-cost UAVs, satellite data and ground sensors. An array of ground sensors, such as fixed towers equipped with infrared cameras and IoT sensors strategically placed in areas with a high probability of wildfire, will work in tandem with the space domain as well as the air domain to generate an accurate and comprehensive flow of information. This system-of-systems approach aims to take advantage of the key benefits across all systems while ensuring seamless cooperation. Having scalability and effectiveness in mind, the system is designed to work with low-cost COTS UAVs that leverage infrared and RGB sensors which will act as the primary situational awareness generator on demand. AI task allocation algorithms and swarming-oriented area coverage methods are at the heart of the system, effectively managing the aerial assets High-level mission planning takes place in the GCS, where information from all sensors is gathered and compiled into a user-understandable schema. In addition, the GCS issues warnings for events such as the detection of fire and hardware failures, live video feed and lower-level control of the swarm and IoT sensors when requested. By performing intelligent sensor fusion, this solution will offer unparalleled reaction times to wildfires while also being resilient and reconfigurable should any hardware failures arise by incorporating state of the art swarming capabilities.
While aerial photography continues to play an integral role in forest management, its data acquisition can now be obtained through an unmanned aerial vehicle (UAV), commonly referred as a drone, instead of conventional manned aircraft. With its feasibility, a drone can be programed to take off, fly over an area following predefined paths and take images, then return to the home spot automatically. When flying over forests, it requires that there is an open space for a vertical takeoff drone to take off vertically and return safely. Hence, the automatic return-to-home feature on the drone is crucial when operating in a woodland landscape. In this project, we assessed the return-to-home landing accuracy based on a permanently marked launch pad nested in a wooded area on the campus of Stephen F. Austin State University in Nacogdoches, Texas. We compared four models of the DJI drone line, with each flown 30 missions over multiple days under different weather conditions. When each drone returned to the home launch spot and landed, the distance and direction from the launch spot to the landing position was measured. Results showed that both the Phantom 4 Advanced and the Spark had superior landing accuracy, whereas the Phantom 3 Advanced was the least accurate trailing behind the Phantom 4 Pro.
In this paper, an autonomous system is developed for drone racing. On account of their vast consumption of computing resources, the methods for visual navigation commonly employed are discarded, such as visual-inertial odometry (VIO) or simultaneous localization and mapping (SLAM). A series of navigation algorithms for autonomous drone racing, which can operate without the aid of the information on the external position, are proposed: one for lightweight gate detection, achieving gates detection with a frequency of 60 Hz; one for direct collision detection, seeking the maximum passability in-depth images. Besides, a velocity planner is adopted to generate velocity commands according to the results from visual navigation, which are enabled to perform a guidance role when the drone is approaching and passing through gates, assisting it in avoiding obstacles and searching for temporarily invisible gates. The approach proposed above has been demonstrated to successfully help our drone passing-through complex environments with a maximum speed of 2.5 m/s and ranked first at the 2022 RoboMaster Intelligent UAV Championship.
Unmanned aerial vehicles (UAVs) have been used to establish flying ad hoc networks (FANETs) to support wireless communication in various scenarios, from disaster situations to wireless coverage extensions. However, the operation of FANETs faces mobility, wireless network variations and topology challenges. Conventional mobile ad hoc network and vehicular ad hoc network routing concepts have rarely been applied to FANETs, and even then they have produced unsatisfactory performance due to additional challenges not found in such networks. For instance, position-based routing protocols have been applied in FANET, but have failed to achieve adequate performance in large networks. Clustering solutions have also been used in large networks, but with a significant overhead in keeping track of the complete topology. Hence, to solve this problem, we propose a hybrid position-based segment-by-segment routing mechanism for clustered FANETs. This approach facilitates traffic engineering across multiple wireless clusters by combining position-based inter-cluster routing with a rank-based intra-cluster routing approach capable of balancing traffic loads between alternative cluster heads. Simulation results show that our solution achieves, on average, a lower power consumption of 72.5 J, a higher throughput of 275 Mbps and a much lower routing overhead of 17.5% when compared to other state-of-the-art end-to-end routing approaches.
Temporary streams are a key component of the hydrological cycle in arid and semi-arid regions, but their flow is highly variable and difficult to measure. In this paper, we present a novel approach that could be used to assess the flow of temporary streams this allowing to characterize their environmental status. Specifically, we apply the Image Velocimetry (IV) method to estimate surface velocity in temporary streams using Unmanned Aerial Vehicles (UAVs) equipped with optical sensors (IV-UAV method). The IV-UAV method enables the easy, safe and quick estimation of the velocity on the water’s surface. This method was applied in different temporary streams in Lesvos Island, Greece. The results obtained indicate that the IV-UAV can be implemented at low discharges, temporary streams and small streams. Specifically, the water depth ranged from 0.02 m to 0.28 m, while the channel width ranged from 0.6 m to 4.0 m. The estimated surface velocity ranged from 0.0 to 5.5 m/s; thus, the maximum water discharge was 0.60 m3/s for the largest monitored stream of the island. However, there were many occasions that measurements were unable due to various reasons such as dense vegetation or archaeological sites. Despite of this, the proposed methodology could be incorporated in optical protocols which are used to assess the environmental status of temporary streams of Mediterranean conditions. Finally, this would become a valuable tool for understanding the dynamics of these ecosystems and monitoring changes over time.
This paper presents a method for fault tolerant control of quadrotor UAVs in case of inversion of the torque direction, a situation that might occur due to structural, hardware or software issues. The proposed design is based on multiple-model ℒ1 adaptive control. The controller is composed of a nominal reference model and a set of degraded reference models. The nominal model is that with desired dynamics that are optimal regarding some specific criteria. In a degraded model, the performance criteria are reduced. It is designed to ensure system robustness in the presence of critical failures. The controller is tested in simulations and it is shown that the multiple model ℒ1 adaptive controller stabilizes the system in case of inversion of the control input, while the ℒ1 adaptive controller with a single nominal model fails.
In this paper, image fusion is performed by utilizing images derived from different cameras for the unmanned aerial vehicle (UAV). By producing the fused image, the spatial resolution of the multispectral (MS) image is improved on the one hand and the classification accuracy on the other hand. First, however, the horizontal and vertical accuracy of the generated products, orthophoto mosaics, and digital surface models, is determined using checkpoints that do not participate in the processing of the image blocks. Also, the changes of these accuracies with a 50% increase (or decrease) of the UAV's flight height are determined. The study area is the Early Christian Basilica C and the flanking Roman buildings, at the archaeological site of Amphipolis (Eastern Macedonia, Greece).