A benchmarking of commercial small fixed-wing electric UAVs and RGB cameras for photogrammetry monitoring in intertidal multi-regions

Small fixed-wing electric Unmanned Aerial Vehicles (UAVs) are perfect candidates to perform tasks in wide areas, such as photogrammetry, surveillance, monitoring, or search and rescue, among others. They are easy to transport and assemble, have much greater range and autonomy, and reach higher speeds than rotatory-wing UAVs. Aiming to contribute towards their future implementation, the objective of this article is to benchmark commercial, small, fixed-wing, electric UAVs and compatible RGB cameras to find the best combination for photogrammetry and data acquisition of mussel seeds and goose barnacles in a multi-region intertidal zone of the south coast of Galicia (NW of Spain). To compare all the options, a Coverage Path Planning (CPP) algorithm enhanced for fixed-wing UAVs to cover long areas with sharp corners was posed, followed by a Traveling Salesman Problem (TSP) to find the best route between regions. Results show that two options stand out from the rest: the Delair DT26 Open Payload with a PhaseOne iXM-100 camera (shortest path, minimum number of pictures and turns) and the Heliplane LRS 340 PRO with the Sony Alpha 7R IV sensor, finishing the task in the minimum time.Agencia Estatal de Investigación | Ref. PID2021-125060OB-I00Agencia Estatal de Investigación | Ref. TED2021-129756B-C31Ministerio de Universidades | Ref. FPU21/01176Universidade de Vig

Comparison of deep learning and analytic image processing methods for autonomous inspection of railway bolts and clips

In this work, different methods are proposed and compared for autonomous inspection of railway bolts and clips. A prototype of an autonomous data acquisition system was developed to automatically obtain information of the state of the railway track using LiDAR and camera sensors. This system was employed in a testing railway track installed in the facilities of the University of Vigo to obtain the images used in this work. Then, the images were further processed using analytic image segmentation algorithms as well as a neural network to detect the bolts and clips. Once these elements are detected, their relative position is computed to evaluate if there is any missing component. Finally, the orientation of the clips is computed to ensure that all the bolts are correctly placed. Four different methods were implemented, and their performance was evaluated using the segmentations provided by the analytical methods and the neural network.Ministerio de Universidades | Ref. FPU21/01176Ministerio de Ciencia e Innovación | Ref. PLEC2021-007940Recovery, Transformation and Resilience Plan of the European Union – NextGenerationEU (University of Vigo) | Ref. 58550

Operational study of drone spraying application of phytosanitary products in vineyards

The use of drones in topics related to precision agriculture to improve the efficiency in the application of phytosanitary products to vineyards increases every day. Drones are especially productive in difficult orographic terrains, where other mechanical systems such as tractors cannot be used. This study shows the development and implementation of a methodology to determine key parameters to decide the suitability of a drone to a spraying task (i.e. spraying time for a certain parcel, number or tank refills required), taking into account the technical specifications of a certain commercial model. For the validation, the data of a vineyard belonging to the Rías Baixas appellation of origin (NW Spain) and the technical specifications of drones from three different manufacturers (i.e. DJI, Hylio and Yamaha) are used. Results show that the Hylio AD122 with a phytosanitary tank of 22 L provides the best performance, with a productivity around 6 minutes per hectare.La utilización de drones en tareas relacionadas con la agricultura de precisión para mejorar la eficiencia en la aplicación de productos fitosanitarios en viñedos es cada vez mayor. Los drones son especialmente eficientes en terrenos con orografía difícil, donde no se pueden emplear otros sistemas mecánicos como tractores. Este estudio muestra el desarrollo e implementación de una metodología para determinar parámetros clave que decidan la adecuación de un drone determinado a una tarea de fumigación (por ejemplo, el tiempo de fumigación para una cierta parcela o el número de tanques requeridos para dicha fumigación), teniendo en cuenta las especificaciones técnicas de un determinado modelo comercial. Para la validación de la metodología, se han utilizado los datos de un viñedo que pertenece a la denominación de origen Rías Baixas (Noroeste de Espala) y las características técnicas de tres fabricantes diferentes de drones (DJI, Hylio y Yamaha). Los resultados obtenidos muestran como el Hylio AD122 con un tanque de fitosanitario de 22 L provee el mejor rendimiento, con una productividad de aproximadamente 6 minutos por hectáre

LiDAR based detect and Avoid system for UAV navigation in UAM corridors

In this work, a Detect and Avoid system is presented for the autonomous navigation of Unmanned Aerial Vehicles (UAVs) in Urban Air Mobility (UAM) applications. The current implementation is designed for the operation of multirotor UAVs in UAM corridors. During the operations, unauthorized flying objects may penetrate the corridor airspace posing a risk to the aircraft. In this article, the feasibility of using a solid-state LiDAR (Light Detecting and Ranging) sensor for detecting and positioning these objects was evaluated. For that purpose, a commercial model was simulated using the specifications of the manufacturer along with empirical measurements to determine the scanning pattern of the device. With the point clouds generated by the sensor, the system detects the presence of intruders and estimates their motion to finally compute avoidance trajectories using a Second Order Cone Program (SOCP) in real time. The method was tested in different scenarios, offering robust results. Execution times were of the order of 50 milliseconds, allowing the implementation in real time on modern onboard computers

Deep learning based target pose estimation using LiDAR measurements in active debris removal operations

In this work, a study on the use of a commercial global-flash LiDAR sensor in Active Debris Removal operations is presented. This type of activity requires precise knowledge of the position and orientation of the target to be removed. For these missions, relative navigation devices such as cameras or LiDAR sensors are typically regarded. In this study, the mission profile defined in the e.Deorbit System Requirements Review was considered and data acquisition and processing from a commercial ASC GSFL-16KS LiDAR sensor were simulated. As the main novelty of this work, the use of Multi-Layer Perceptron neural networks for the processing of LiDAR depth images is proposed in order to obtain an estimate of the pose of the target. Using the results of the neural networks, an Iterative Closest Point (ICP) algorithm is applied to refine the calculation of the pose. The accuracy and computation time of the system were evaluated, obtaining robust and computationally efficient results in the proposed study cases.Ministerio de Universidades | Ref. FPU21/01176Universidade de Vig