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

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Open-Access Platform for the Simulation of Aerial Robotic Manipulators

2024 , Varela Aldas, José , Recalde, Luis F. , Guevara B.S. , Andaluz V.H. , Gandolfo D.C.

Recent technological advances have brought increased attention to aerial robotic manipulators (ARMs), particularly in applications that involve physical interactions using tools such as welding and drilling, as well as in the autonomous pickup and transport of objects. However, translating control algorithms into real-world applications for aerial robotic manipulators may prove challenging, given the potential for accidents and the time-consuming nature of experiments; furthermore, the acquisition of aerial robotic manipulators could impose a substantial financial burden on universities, research centers, and companies. Therefore, this work addresses these issues by developing an open access platform to simulate aerial robotic manipulators and test control strategies. The presented simulator is based on the kinematics and dynamics of the Matrice-100 aerial platform equipped with a 3 DOF robotic arm, where the mathematical formulation was developed using the Euler-Lagrange formalism. In addition, optimization techniques were used to perform the parameter identification procedure, ensuring the development of an accurate model for the open-access platform. The simulator platform is built upon the integration of Python, the Robot Operating System (ROS), and Unity 3D. These components collaborate to describe and demonstrate the behavior of the aerial robotic manipulator during the test process of control system algorithms. Simple tests were conducted to validate the open-access simulator platform. The proposed approach ensures the evaluation, testing of control strategies, and the ability to conduct experiments before hardware implementations. Finally, the proposal was published as an open source platform available in the following Code. © 2013 IEEE.

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Constrained Visual Servoing of Quadrotors Based on Model Predictive Control

2022 , Recalde, Luis F. , Varela Aldas, José , Guevara B.S. , Andaluz V. , Gimenez J. , Gandolfo D.

One of the main issues of visual servoing schemes occurs when the target objects leave out the field of view (FOV) of the camera, which causes failure or poor performance of the controller. Solving this problem can be a challenge due to traditional controllers cannot include system's constraints. This work presents model predictive control (MPC) for constrained image-based visual servoing (IBVS) applied in quadrotors, considering constraints in FOV and restrictions in the control actions. To handle with image constraints the MPC considers: (1) the target objects stay only in camera's FOV and this work converts these restrictions in state constraints, (2) merge image instantaneous kinematics and the dynamic of commercial quadrotors (Mavic Pro 2) in a general mathematical model in order to satisfy the bounded control actions and image constraints. Due to commercial quadrotors allow velocities like control inputs, this work considers the reduced dynamic model in general velocities space and it was identified using Dynamic Mode Decomposition with control (DMDc) algorithm. This work uses Webots to evaluate the performance of the proposed controller. Finally the controller is compared with a classical IBVS scheme in order to verify the efficacy of the proposed controller and systematically evaluate the performance considering the system constraints. © 2022 Elsevier B.V.. All rights reserved.

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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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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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Adaptive NMPC-RBF with Application to Manipulator Robots

2023 , Recalde, Luis F. , Varela J. , Guevara B.S. , Andaluz V. , Gandolfo D.

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. © 2023 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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