SÁNCHEZ MUYULEMA, LUIS MIGUEL

Preferred name
SÁNCHEZ MUYULEMA, LUIS MIGUEL
Main Affiliation
GISER - Grupo de Investigación en Sistemas Embebidos y Robótica
Web Site
ORCID
0000-0001-5078-3734
Scopus Author ID
57198646872

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Now showing 1 - 6 of 6
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    Analysis and Construction of a Mobile Robot for Search and Mitigation of Low-Scale Fire
    (IEEE, 2017-11)
    SANCHEZ MUYULEMA, LUIS MIGUEL  
    ;
    Vladimir V. Bonilla
    ;
    Marcelo C. Moya
    ;
    Guillermo C. Mosquera
    Mobile robotic platform that allows the search of people after an earthquake has occurred along with a low-scale fire mitigation system was presented. The mechanical, electrical-electronic system and a wireless control interface for the operation and transmission of video of the mobile robot have been developed. The design of the mechanical system allowed the implementation of a mobile platform that enters complex terrains and a robotic arm with 4 degrees of freedom whose functions are the delivery of first aid kits and the manipulation of the fire mitigation system. The electrical-electronic system is in charge of controlling the electromechanical actuators of the system. The supervision and monitoring system uses two cameras on the robot that transmit the video to external monitors or mobile devices. This allows safeguard the integrity of rescuers by supporting them in their search and assistance tasks providing a technological alternative to the teams dedicated to solve this problems. Field tests were carried out at the training and training center of the public company EMBA-EP, which allowed validating the operation of the platform.
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    Upper Limb Exoskeleton Design and Implementation to Control a Robotic Arm
    (IEEE, 2017-11)
    Marlon Morales U
    ;
    Guillermo Mosquera C
    ;
    SANCHEZ MUYULEMA, LUIS MIGUEL  
    ;
    William Chamorro H.
    This project presents the design, modelling and implementation of a system, which allows controlling a robotic arm through movement commands captured by an exoskeleton adapted to an upper limb. A study of degrees of freedom, joint movement angles and limitations that will have both the robotic arm and the exoskeleton was performed. The mechatronics design methodology based on the V-Model was used. The mechanical, electrical and control designs were developed concurrently in a way that one does not compromise the other's integrity. The mechanical design is realized and simulated in a CAD software to verify the operation and movement freedom that the user will have. Batteries are dimensioned to power the exoskeleton and the robotic arm. After integration of the systems designed, requirements assurance is performed. The robotic arm is able to replicate the movements made with the exoskeleton; the reaction time in slow movements is in real time, while for high reflections this present a delay up to 0.42 seconds. The robotic arm is able to lift 250 g without compromising its reaction speed. The ratio of movements between robotic arm and exoskeleton has a tolerance of up to 5 degrees.
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    Comparison of Two System Identification Approaches for a Four-Wheel Differential Robot Based on Velocity Command Execution
    (MDPI AG, 2025-06-05)
    GUFFANTI MARTINEZ, DIEGO ANDRES  
    ;
    Moisés Filiberto Mora Murillo
    ;
    Marco Alejandro Hinojosa
    ;
    Santiago Bustamante Sanchez
    ;
    Javier Oswaldo Obregón Gutiérrez
    Precise modeling of differential drive robots is crucial for effective control and trajectory planning in autonomous systems. A comparative analysis of two modeling approaches for a four-wheel differential drive robot is presented in this paper. The first approach, named Motor-Based Model (MBM), identifies four transfer functions, one for each motor, while the second approach, named Simplified Model (SM), uses only two transfer functions, one for linear velocity and another for angular velocity. Both models were validated by comparing their predicted trajectories against real odometry data obtained from a SLAM system implemented on a differential-drive robot. This provided a practical assessment of each model’s accuracy and underscored the importance of model selection in control design and navigation tasks. The results showed that the Motor-Based Model (MBM) consistently outperformed the Simplified Model (SM) in terms of odometry accuracy, both in position and orientation. Across all trajectories, the average RMSE for position using MBM was 0.309 m, while the SM recorded a higher average RMSE of 0.414 m. Similarly, the maximum position error averaged 0.522 m for MBM and 0.710 m for SM, confirming that MBM is more accurate and consistent in position tracking. Regarding the results of orientation estimation, when averaged across all experiments, the MBM maintained a lower angular RMSE of 0.170 rad in contrast to SM, which achieves an RMSE of 0.239 rad. The maximum angular error was also higher for the MBM at 0.316 rad, compared to 0.447 rad for the SM. Moreover, the computational performance evaluation indicated that the SM consistently outperformed MBM, achieving a 30% reduction in simulation time and substantially lower memory usage. These results demonstrate the relationship between model complexity and accuracy and suggest that the motor-specific model is more appropriate for applications requiring precise mapping or localization, such as SLAM, while the simplified model may be suitable for simpler use cases with lower computational requirements, such as embedded systems with limited resources. This paper provides a practical evaluation of the accuracy and computational performance of two modeling approaches, highlighting the implications of model selection for the design of navigation tasks.
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    Optimization of Fault Prediction by A.I. in Industrial Equipment: analysis of the operating parameters of a Bench Grinder
    (AG Editor (Argentina), 2025-03-18)
    GUTIERREZ SUQUILLO, NELSON RAMIRO  
    ;
    CHAMBA CRUZ, JONNATHAN ISMAEL  
    ;
    Christiam Xavier Núñez
    ;
    SANCHEZ MUYULEMA, LUIS MIGUEL  
    ;
    Rafael Christian Franco Reina
    Predictive Maintenance (PM) plays a crucial role in maximizing efficiency and reducing costs associated with equipment and system maintenance in industrial companies. Recent advancements in Machine Learning (ML) have revolutionized PM by offering accurate and efficient fault prediction and maintenance planning capabilities. This research focuses on monitoring a bench grinder and observing sensors for temperature, current, angular velocity, and vibration under normal operating conditions. The objective is to predict failures based on specific variables related to the machine. To develop the system, a prototype bench was designed to subject the machine to several working scenarios, collecting real-time sensor data. Data clusters were generated for each sensor, collecting 3000 samples over 7 consecutive days without faults and another 7 days with modified bench grinder behavior. Sampling was done at a rate of 1 second. The performance of Decision Trees (DT), Support Vector Machines (SVM), Naive Bayes (NB), and K-Means + Neural Network (NN) algorithms was compared using the confusion matrix metrics. Each algorithm's performance was evaluated for RPM, current, temperature, and vibrations measures. The SVM algorithm showed the highest error for RPM with 43.5%. In contrast, all algorithms achieved minimal or zero errors for vibrations, indicating excellent performance. These findings demonstrate the potential of ML algorithms in PM for the bench grinder. The results highlight the importance of selecting appropriate algorithms for specific measurements, with vibrations exhibiting the least error across all algorithms and contributes to optimize maintenance activities in industrial settings.
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    Application of Model-Based Design for Filtering sEMG Signals Using Wavelet Transform
    (AG Editor (Argentina), 2025-02-13)
    Vladimir Bonilla Venegas
    ;
    Guillermo Mosquera Canchingre
    ;
    Miguel Sánchez Muyulema  
    ;
    Jonnathan Ismael Chamba Cruz
    ;
    Nelson Gutiérrez Suquillo  
    The aim of this study was the integration of model-based design and Wavelet transform techniques for filtering surface electromyography (sEMG) signals. In the first stage the noises and interferences that disturb sEMG signals were analyzed to implement a digital filter in a low-cost embedded system that filters these signals. It was shown that the noises and interferences are caused by various sources. Sources of interference and noise can be divided into internal and external. Internal noise is caused by the electrodes, EMG signals of other muscles, and noise associated with the functioning of other organs such as the heart or stomach. The external noises are due to the electrical environment, the most prominent of which is the direct interference of the power hum, produced by the incorrect grounding of other devices and electromotors. For the analysis of the digital filter, sEMG signals from the biceps muscle were used when the elbow joint was at rest and during flexion and extension movements. Signals from 10 participants who did not have any atrophies or pathologies in the muscle were considered for this stage. Denoising of sEMG signals was performed using different wavelets; the smallest error was observed when using the biorthogonal wavelet 3/5 of level 6 with the soft thresholding method. The wavelet filter was implemented using the V-model, and the Processor in The Loop (PIL) tests helped to determine the characteristics of the embedded system where the digital filter was implemented. The digital filter code was implemented on an ESP32 board due to its processing speed of 328 ms.
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    Robot Animation in a Virtual Reality Environment
    (IEEE, 2018-11)
    SANCHEZ MUYULEMA, LUIS MIGUEL  
    ;
    Marcelo Moya
    ;
    CHAMBA CRUZ, JONNATHAN ISMAEL  
    ;
    Paola Granizo
    The aim of this work is develop and configure a three-dimensional animation of a robot in virtual reality environment using the Unity software. The movement of the robots uses mechanical principles which considers that the degrees of freedom must be controlled independently through a microcontroller. In addition, the necessary steps are shown to transfer the three-dimensional model of a robot developed in a CAD tool to a virtual simulation environment without losing the physical characteristics of the original design. In the analysis of results, the simulation is shown in a virtual environment, considering real physical parameters together with the movement of a hexapod robot of 18DOF.