Recalde, Luis F.
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Preferred name
Recalde, Luis F.
Main Affiliation
Ambato
Email
fernandorecalde@uti.edu.ec
ORCID
Scopus Author ID
57190424410

Research Output
Now showing 1 - 3 of 3
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Publication

Visual Servoing NMPC Applied to UAVs for Photovoltaic Array Inspection

2024 , Velasco-Sanchez E. , Recalde, Luis F. , Guevara B.S. , Varela-Aldas J. , Candelas F.A. , Puente S.T. , Gandolfo D.C.

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. © 2016 IEEE.

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Obstacle Detection with Differences of Normals in Unorganized Point Clouds for Mobile Robotics

2023 , Velasco-Sánchez E. , Olivas A. , Recalde, Luis F. , Guevara B.S. , Candelas F.A.

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. © 2023 IEEE.

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Quadratic Programming Optimization Applied to Robotics Systems

2023 , Recalde, Luis F. , Velasco-Sánchez E. , Guevara B.S. , Gandolfo D. , Candelas F.A.

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. © 2023 IEEE.

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