UAV Bluetooth Communication Link Assessment for Emergency Response Applications

Over the last number of years Unmanned Aerial Vehicles (UAVs) have become increasingly popular in several fields such as agriculture and medicine. Recently UAVs have been used for the deployment within Emergency Response for visual scouting of an impacted area in addition to delivering supplies via payload. UAVs are also capable of acting as networking nodes using incorporated technology or by attaching independent hardware via the UAVs payload capability. The application of UAVs as a network node(s) can enable increased performance within the network as UAV based nodes can alter their current position due to their unique nature of mobility. Therefore, this study aims to assess the performance of aerial communication using UVAs which incorporate the use of a small Bluetooth 5.0 (BLE 5.0) node as a payload. In this research, the impact generated via Air-Ground Propagation on the Received Signal Strength Indicator (RSSI) measurement was investigated. In addition to this, this paper also investigates any performance difference displayed between different Transmission Power Profiles as this in turn affects the overall power consumption of the transmitting device. When operating at heights of 5, 10, and 15 Metres the maximum average loss was found to be 2.37 dBm among the Standard Transmission Power profile and a maximum average loss of 1.18 dBm occurring with the Enhanced Transmission Power Profile

An iterative algorithmic UAV path optimization process for Structure-for-Motion modelling

The use of unmanned aerial vehicles (UAVs) for 3D reconstruction through photogrammetry has gained significant attention in recent years. With the advancement of technology and the availability of affordable drones with high-resolution cameras, capturing aerial images for creating detailed 3D models has become more accessible, however, UAV survey flight planning still presents challenges. The planning stage is essential in aerial photogrammetry as it sets the foundation for efficient and accurate surveying. Proper predictive planning ensures a smooth workflow on site, generating high-quality datasets for reconstruction while minimizing the need for repeat surveys. This approach not only reduces costs but also mitigates potential errors and delays during the survey process. Within the presented frame of reference, the present study explores the use of UAVs for 3D reconstruction through photogrammetry, focusing on optimizing flight paths and view planning. It addresses challenges such as safety, navigation, and image dataset optimization. The study presents the current advancement of custom parametric workflow developed in Rhino/Grasshopper. The workflow is targeted for average users, aiming to simplify the process and integrate it with architectural and planning workflows. The approach involves four algorithms, including proxy model generation, visibility analysis, path generation, and camera position estimation. The iterative process enhances precision through progressive refinement of the proxy model, offering potential for predictive modelling and effective photogrammetry utilization in UAV planning. Further research and testing are needed to validate real-world performance

The UJI Aerial Librarian Robot: A Quadcopter for Visual Library Inventory and Book Localisation

Over time, the field of robotics has provided solutions to automate routine tasks in different scenarios. In particular, libraries are awakening great interest in automated tasks since they are semi-structured environments where machines coexist with humans and several repetitive operations could be automatically performed. In addition, multirotor aerial vehicles have become very popular in many applications over the past decade, however autonomous flight in confined spaces still presents a number of challenges and the use of small drones has not been reported as an automated inventory device within libraries. This paper presents the UJI aerial librarian robot that leverages computer vision techniques to autonomously self-localize and navigate in a library for automated inventory and book localization. A control strategy to navigate along the library bookcases is presented by using visual markers for self-localization during a visual inspection of bookshelves. An image-based book recognition technique is described that combines computer vision techniques to detect the tags on the book spines, followed by an optical character recognizer (OCR) to convert the book code on the tags into text. These data can be used for library inventory. Misplaced books can be automatically detected, and a particular book can be located within the library. Our quadrotor robot was tested in a real library with promising results. The problems encountered and limitation of the system are discussed, along with its relation to similar applications, such as automated inventory in warehouses.Support for the research conducted at UJI Robotic Intelligence Laboratory is provided in part by the Ministerio de Economía y Competitividad (DPI2015-69041-R), by Universitat Jaume I (UJI-B2018-74), and by Generalitat Valenciana (PROMETEO/2020/034, GV/2020/051)

Drone Flight Path Architecture

This project was built from a pre-existing architecture that facilitates the planning and automatic execution of drone routes in a known space through a 3D virtual reality environment. Our work consisted in extending this architecture by integrating a new web component, making use of a 3D map API, to facilitate to people who do not have access to virtual reality hardware, the possibility of planning flight routes that have as parameters the with respect to the globe and also a component in meters that represents the height at which the drone rises in a certain point. Additionally, the configuration possibilities of a route were extended in order to take advantage of one of the components that gives more value and potential to unmanned aircrafts: the use of the camera in multiple contexts and scenarios. The extension of this solution allows the user to assign different camera tasks along the route, see in real time what the camera is capturing and, after the flight, retrieve the multimedia content that was createdComment: Language: Spanish, Lenguaje:Espa\~nol. Codigo de la web app disponible en https://github.com/dennis-forero/3D-drone-route-planne

Learning-based wildfire tracking with unmanned aerial vehicles

This project attempts to design a path planning algorithm for a group of unmanned aerial vehicles (UAVs) to track multiple spreading wildfire zones on a wildland. Due to the physical limitations of UAVs, the wildland is partially observable. Thus, the fire spreading is difficult to model. An online training regression neural network using real-time UAV observation data is implemented for fire front positions prediction. The wildfire tracking with UAVs path planning algorithm is proposed by Q-learning. Various practical factors are considered by designing an appropriate cost function which can describe the tracking problem, such as importance of the moving targets, field of view of UAVs, spreading speed of fire zones, collision avoidance between UAVs, obstacle avoidance, and maximum information collection. To improve the computation efficiency, a vertices-based fire line feature extraction is used to reduce the fire line targets. Simulation results under various wind conditions validate the fire prediction accuracy and UAV tracking performance.Includes bibliographical references