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1تقرير
المؤلفون: Velasco-Sánchez, Edison P., Recalde, Luis F., Guevara, Bryan S., Varela-Aldás, José, Candelas, Francisco A., Puente, Santiago T., Gandolfo, Daniel C.
مصطلحات موضوعية: Computer Science - Robotics
الوصف: The photovoltaic (PV) industry is seeing a significant shift toward large-scale solar plants, where traditional inspection methods have proven to be time-consuming and costly. Currently, the predominant approach to PV inspection using unmanned aerial vehicles (UAVs) is based on photogrammetry. However, the photogrammetry approach presents limitations, such as an increased amount of useless data during flights, potential issues related to image resolution, and the detection process during high-altitude flights. In this work, we develop a visual servoing control system applied to a UAV with dynamic compensation using a nonlinear model predictive control (NMPC) capable of accurately tracking the middle of the underlying PV array at different frontal velocities and height constraints, ensuring the acquisition of detailed images during low-altitude flights. The visual servoing controller is based on the extraction of features using RGB-D images and the Kalman filter to estimate the edges of the PV arrays. Furthermore, this work demonstrates the proposal in both simulated and real-world environments using the commercial aerial vehicle (DJI Matrice 100), with the purpose of showcasing the results of the architecture. Our approach is available for the scientific community in: https://github.com/EPVelasco/VisualServoing_NMPCTest
Comment: This paper has been accepted for publication in IEEE Robotics and Automation Lettersالوصول الحر: http://arxiv.org/abs/2311.08019Test
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2دورية أكاديمية
المؤلفون: Guevara, Bryan S., Recalde, Luis F., Moya, Viviana, Varela-Aldás, José, Gandolfo, Daniel C., Toibero, Juan M.
المساهمون: Universidad Indoamérica, Universidad Internacional del Ecuador, Instituto de Automática, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de San Juan
المصدر: IEEE Access ; volume 12, page 77121-77132 ; ISSN 2169-3536
الإتاحة: https://doi.org/10.1109/access.2024.3407684Test
http://xplorestaging.ieee.org/ielx8/6287639/10380310/10542329.pdf?arnumber=10542329Test -
3دورية أكاديمية
المؤلفون: Velasco, Edison P., Recalde, Luis F., Guevara, Bryan S., Varela-Aldás, José, Candelas-Herías, Francisco A., Puente Méndez, Santiago T., Gandolfo, Daniel C.
المساهمون: Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal, Universidad de Alicante. Instituto Universitario de Investigación Informática, Automática, Robótica y Visión Artificial
مصطلحات موضوعية: Aerial systems: Perception and autonomy, Optimization and optimal control, Visual servoing
الوصف: The photovoltaic (PV) industry is seeing a significant shift toward large-scale solar plants, where traditional inspection methods have proven to be time-consuming and costly. Currently, the predominant approach to PV inspection using unmanned aerial vehicles (UAVs) is based on the capture and detailed analysis of aerial images (photogrammetry). However, the photogrammetry approach presents limitations, such as an increased amount of useless data and potential issues related to image resolution that negatively impact the detection process during high-altitude flights. In this work, we develop a visual servoing control system with dynamic compensation using nonlinear model predictive control (NMPC) applied to a UAV. This system is capable of accurately tracking the middle of the underlying PV array at various frontal velocities and height constraints, ensuring the acquisition of detailed images during low-altitude flights. The visual servoing controller is based on extracting features using RGB-D images and employing a Kalman filter to estimate the edges of the PV arrays. Furthermore, this work demonstrates the proposal in both simulated and real-world environments using the commercial aerial vehicle (DJI Matrice 100), with the purpose of showcasing the results of the architecture. ; This work was supported in part by the Ministry of Science and Innovation of the Spanish Government under the research Project PID2021-122685OBI00 and in part by the training of research Ph.D. staff under Grant PRE2019-088069.
العلاقة: http://doi.org/10.1109/LRA.2024.3360876Test; info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2021-122685OB-I00; IEEE Robotics and Automation Letters. 2024, 9(3): 2766-2773. http://doi.org/10.1109/LRA.2024.3360876Test; http://hdl.handle.net/10045/140752Test
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4دورية أكاديمية
المؤلفون: Varela-Aldás, José, Recalde, Luis F., Guevara, Bryan S., Andaluz, Victor H., Gandolfo, Daniel C.
المساهمون: Universidad Indoamérica, Ecuador
المصدر: IEEE Access ; volume 12, page 49735-49751 ; ISSN 2169-3536
مصطلحات موضوعية: General Engineering, General Materials Science, General Computer Science, Electrical and Electronic Engineering
الإتاحة: https://doi.org/10.1109/access.2024.3384986Test
http://xplorestaging.ieee.org/ielx7/6287639/10380310/10491265.pdf?arnumber=10491265Test -
5تقرير
المؤلفون: Langedijk, Anna, Dankers, Verna, Lippe, Phillip, Bos, Sander, Guevara, Bryan Cardenas, Yannakoudakis, Helen, Shutova, Ekaterina
مصطلحات موضوعية: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
الوصف: Meta-learning, or learning to learn, is a technique that can help to overcome resource scarcity in cross-lingual NLP problems, by enabling fast adaptation to new tasks. We apply model-agnostic meta-learning (MAML) to the task of cross-lingual dependency parsing. We train our model on a diverse set of languages to learn a parameter initialization that can adapt quickly to new languages. We find that meta-learning with pre-training can significantly improve upon the performance of language transfer and standard supervised learning baselines for a variety of unseen, typologically diverse, and low-resource languages, in a few-shot learning setup.
Comment: - Add additional results (Appendix D) - Cosmetic updates for camera-ready version ACL 2022الوصول الحر: http://arxiv.org/abs/2104.04736Test
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6دورية أكاديمية
المؤلفون: Recalde, Luis, Velasco-Sánchez, Edison, Guevara, Bryan, Gandolfo, Daniel, Candelas, Francisco
الوصف: Reactive kinematic control in velocity space is closely related to the Jacobian presented in the system. However, if the Jacobian is rank-deficient, certain task-space velocities become unachievable, leading to controller degradation. This work presents an approach to controlling robotic systems based on a quadratic programming formulation that yields solutions in just a few milliseconds. The proposed framework is tested in simulation using a two-link planar robotic arm and a mobile robot. Simulations are implemented in MuJoCo and Webots, respectively, to demonstrate the efficiency of the formulation.
وصف الملف: application/vnd.openxmlformats-officedocument.wordprocessingml.document
العلاقة: https://ieeexplore.ieee.org/document/10309064Test; https://repositorio.uti.edu.ec//handle/123456789/6174Test
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7دورية أكاديمية
المؤلفون: Velasco-Sánchez, Edison, Olivas, Alejandro, Recalde, Luis, Guevara, Bryan, Candelas, Francisco
الوصف: In mobile robotics, there is an increasing need for algorithms that accurately identify in real-time the environment in which a robot is operating, especially when these environments are unstructured. Thus, identifying a safe navigation path is a critical aspect to ensure the safety and smooth operation of autonomous robots. In this paper, we present an algorithm for mobile robotics that identifies potential obstacles in an unstructured environment using the Difference of Normals in the point cloud generated by a 3D LiDAR sensor. The aim of our algorithm is to detect obstacles from point clouds with a fast, low-complexity approach that is specifically designed for autonomous driving applications. Our method has been shown to identify obstacles in real-time and differs from the rest of the state-of-the-art by accurately distinguishing obstacles such as potholes and sidewalks from sloping and unevenness terrain. The algorithm has been successfully tested on an ackermann robot equipped with a 128-layer Ouster OS1 LiDAR sensor. The processing time of our system is 52 ms.
وصف الملف: application/vnd.openxmlformats-officedocument.wordprocessingml.document
العلاقة: https://ieeexplore.ieee.org/document/10309038Test; https://repositorio.uti.edu.ec//handle/123456789/6172Test
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8دورية أكاديمية
الوصف: Control algorithms must deal with model uncertainties and disturbances, making them perfect for real-world applications. In addition, increased computational power in the industrial field allows the implementation of advanced control algorithms such as nonlinear model predictive control (NMPC), which is an optimal control scheme that includes system and control constraints imposed by robot dynamics and the environment. Nevertheless, modeling the robot and its environment is a complicated task due to high nonlinearities, such as model uncertainties in the form of complex unmodeled dynamics, varying payloads, and parameter mismatch, leading to fast degradation of NMPC; therefore, online adaptation laws that improve the performance even in unknown environments are needed. Due to the facts presented before, this work combines the universal approximation of RBFNN and the optimality offered by NMPC in a unified adaptive framework that guarantees good performance even under uncertainties and unmodeled dynamics. The proposed framework is tested in simulation using a 2-link planar robotic arm (SCARA Bosch SR-800), where optimization techniques were used to identify the robot's dynamics. Finally, a comparison of the proposed architecture with a baseline nominal NMPC is made with particular attention to trajectory tracking performance, demonstrating the reduction of the tracking error over non-adaptive NMPC.
وصف الملف: application/vnd.openxmlformats-officedocument.wordprocessingml.document
العلاقة: https://ieeexplore.ieee.org/document/10284283Test; https://repositorio.uti.edu.ec//handle/123456789/6101Test
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9دورية أكاديمية
المؤلفون: Guevara, Bryan, Recalde, Luis, Varela-Aldás, José, Andaluz, Víctor, Gandolfo, Daniel, Toibero, Juan
الوصف: Transport, rescue, search, surveillance, and disaster relief tasks are some applications that can be developed with unmanned aerial vehicles (UAVs), where accurate trajectory tracking is a crucial property to operate in a cluttered environment or under uncertainties. However, this is challenging due to high nonlinear dynamics, system constraints, and uncertainties presented in cluttered environments. Hence, uncertainties in the form of unmodeled dynamics, aerodynamic effects, and external disturbances such as wind can produce unstable feedback control schemes, introducing significant positional tracking errors. This work presents a detailed comparative study between controllers such as nonlinear model predictive control (NMPC) and non-predictive baseline feedback controllers, with particular attention to tracking accuracy and computational efficiency. The development of the non-predictive feedback controller schemes was divided into inverse differential kinematics and inverse dynamic compensation of the aerial vehicle. The design of the two controllers uses the mathematical model of UAV and nonlinear control theory, guaranteeing a low computational cost and an asymptotically stable algorithm. The NMPC formulation was developed considering system constraints, where the simplified dynamic model was included; additionally, the boundaries in control actions and a candidate Lyapunov function guarantees the stability of the control structure. Finally, this work uses the commercial simulator DJI brand and DJI Matrice 100 UAV in real-world experiments, where the NMPC shows a reduction in tracking error, indicating the advantages of this formulation.
العلاقة: https://www.mdpi.com/2504-446X/7/2/144Test; https://repositorio.uti.edu.ec//handle/123456789/4838Test
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10دورية أكاديمية
المصدر: Journal of Positive Psychology and Wellbeing; Vol. 7 No. 1 (2023): Journal of Positive Psychology and Wellbeing; 1074-1091 ; 2587-0130
الوصف: The present research aims to analyze the incidence of the COVID 19 pandemic in the tax revenues of the Ecuadorian state, for which an analysis of tax collection has been made. The research is descriptive and analyzes the information of the collection statistics of the Internal Revenue Service, a comparative study is carried out of each of the taxes between the periods January - July 2019 and January-July 2020, in which a notable decrease in the state's economic resources is evidenced, with the most affected taxes being the Value Added Tax (IVA ), the Income Tax (IR) and the Special Consumption Tax (ICE). It is concluded that the COVID 19 pandemic has considerably affected tax collection in Ecuador causing, among other factors, the slowdown of the country's economy.
وصف الملف: application/pdf
