Centro de investigación en Mecatrónica y Sistemas Interactivos

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Universidad Indoamérica

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From simulation to reality: a learning methodology for understanding control systems and robotic arms with the ARCHIE robot
(Frontiers Media SA, 2026-04-10)
Saeteros, Juan
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Mendoza, Erick
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Ramirez, Jefferson
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Fajardo-Pruna, Marcelo
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Buele, Jorge
Bridging the gap between theoretical concepts and real-world implementation remains a critical challenge in the teaching of control systems and robotics. This paper presents an education method using ARCHIE, a low-cost, open-source articulated robot with six degrees of freedom, as an educational platform designed to enhance students’ understanding of control theory through practical learning. The study combines simulation (using MATLAB’s Simscape) with hands-on practice using the physical robot, following a Sim2Real methodology. A two-hour workshop was conducted with undergraduate mechatronics engineering students, incorporating pre- and post-assessments to evaluate learning gains. Preliminary results suggest a statistically significant improvement in students’ understanding of PID control concepts, with average test scores rising from 46.6% to 63.3% (p = 0.0454, two-tailed). The study also highlights the benefits of active learning, allowing students to experience real-world control system behavior and reflect on discrepancies between simulations and physical implementation. ARCHIE proves to be a valuable educational tool for fostering deeper comprehension of control systems in engineering curricula. © 2026 Saeteros, Mendoza, Ramirez, Fajardo-Pruna, Buele, Yumbla and Saldarriaga.
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Real-Time Industrial Risk Mitigation through an IIoT-Based Acoustic Alert System with Embedded Edge Processing and Remote Monitoring
(Springer Nature Switzerland, 2026)
Vargas-Bustamante, Miguel
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Álvarez-Tello, Jorge
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Cevallos-Sornoza, Angie
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Zapata, Mireya
Environmental monitoring schemes have been transformed by the incorporation of IoT technologies in industrial environments, enabling a shift toward continuous monitoring models and immediate response to risk situations. However, many available solutions rely on highly complex and costly SCADA infrastructures, which limits their adoption in facilities with budgetary constraints. In this context, the present research posed as its central question whether it is possible to design and implement a low-cost IoT system, based on open-source technologies, capable of providing real-time environmental monitoring with immediate acoustic alerts and reliable remote notifications. To address this question, an experimental architecture was developed based on Arduino and ESP32 platforms, integrating temperature and gas sensors, a DFPlayer Mini module for customized acoustic feedback, and a wireless transmission scheme to a remote platform. The system was validated through controlled tests involving the exceedance of critical thresholds, evaluating detection accuracy, alert activation latency, and communication stability during continuous operation. The results showed a correct detection rate above 95%, an average alert activation time below 1.5 s, and remote transmission stability greater than 97%. In addition, an approximate 40% reduction in operator response time was observed compared to traditional manual schemes. Consequently, the study demonstrates that an open and scalable IoT architecture can achieve adequate performance levels for industrial safety, contributing a robust human–computer interaction approach and establishing a solid foundation for future extensions toward predictive and interoperable capabilities.
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J-DEAS: A Jamming-Driven Exponential Adaptive Sleeping Technique for Energy-Aware Mitigation in LoRa Networks
(MDPI AG, 2026-07-02)
Del-Valle-Soto, Carolina
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Mex-Perera, Carlos
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Velazquez, Eduard
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Varela Aldas, José
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Valdivia, Leonardo J.
Low-Power Wide-Area Networks based on LoRa are widely deployed in smart city, agricultural, and environmental monitoring, where their constrained energy budget makes them vulnerable to radio-frequency jamming. (1) Background: a node that keeps transmitting into a jammed channel wastes energy on undeliverable packets, yet detection and energy management are usually treated separately. (2) Methods: we present J-DEAS, a Jamming-Driven Exponential Adaptive Sleeping technique that couples a lightweight, threshold-based detector with an exponential sleep back-off scheduler. The detector uses only the RSSI and SNR reported by commodity transceivers, and a single exponentially weighted confidence variable drives the sleep interval; we analyze the decision boundary, confidence dynamics, steady-state duty cycle, and latency–energy trade-off in closed form. (3) Results: on a measurement dataset the detector reaches an AUC of 0.985 and an F1 of 0.969; under sustained jamming, J-DEAS cuts the duty cycle from 100% to 5.5% and wasted transmissions from 94.3% to 8.3%, with a single-slot median latency and a sub-2% false-sleep rate on clean channels. (4) Conclusions: the technique needs no training and no extra hardware, making it suitable for resource-constrained end devices. © 2026 by the authors.
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From Simulation to Reality: A Digital Twin Implementation of a 4-DOF Robotic Manipulator
(2025)
Bryan S. Guevara
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Varela Aldas, José
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Viviana Moya
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Angéelica Veróonica Quito Carrióon
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Marcelo Ortiz
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Implementation of Convolutional Neural Networks for Object Detection in Robotic Pick-and-Place Processes
(Springer Nature Switzerland, 2026)
Villazón, Renato
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Pilco, Andrea
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Prado, Alvaro Javier
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Moya, Viviana
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Chicaiza Claudio, Fernando
Pick-and-place (P&P) tasks in robotic applications have attracted considerable interest for several years. As these are repetitive tasks that are performed continuously and uninterruptedly, any slight improvement in the efficiency of this process results in a significant increase of productivity. In this work, we propose improving a pick-and-place task performed by two Dobot Magician educational manipulator robots alongside a conveyor belt. This system natively incorporates a hardware-based color sensor that allows the robot to identify and classify objects on a conveyor belt, organizing and palletizing them in specific columns according to their color characteristics. However, the robot spends time picking up the objects, placing them on the sensor, and then beginning palletization. In this work, we propose using the Faster R-CNN object detection model with ResNet-50 for feature extraction as an external and independent vision system to streamline and reduce the time losses associated with the hardware-based color sensor tasks. The implemented model achieved a mean Average Precision (mAP) of 68.0% during validation and 65.32% in real-world operation. The system accurately detects all key components involved in the task, including robotic arms, end effectors, the conveyor belt, the color sensor, and the colored blocks (red, green, and blue). When compared to the hardware-based color sensor method, the proposed approach reduced the total processing time from 249.31 s to 196.65 s, representing an improvement of 52.66 s, or approximately 21.12% in overall efficiency. This demonstrates the potential and promising results of using deep learning models in pick-and-place-related applications.
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Does the use of dedicated mobile devices in magnetism classes improve student learning?
(Frontiers Media SA, 2026-04-21)
Varela Aldas, José
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Collay, Washington
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Del-Valle-Soto, Carolina
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Palacios-Navarro, Guillermo
Mobile devices have been evaluated from multiple perspectives as tools to support classroom learning; however, devices specifically dedicated to learning activities still require further research to elucidate their influence in the classroom. This study aimed to evaluate whether the use of dedicated mobile devices for teaching basic magnetism content improves learning among secondary school students. A quantitative, comparative quasi-experimental study with non-equivalent groups was conducted ((Formula presented)) with secondary school students in Ecuador. The experimental group used an educational application running on a dedicated mobile device (M5Stack Core2) and interacted with an electromagnet as a didactic peripheral, whereas the control group received a traditional teacher-centered lesson supported by slides. Learning outcomes were measured using a teacher-designed theoretical test administered at the end of a 90-min session. Mean scores were similar between groups (experimental: 7.32; control: 7.58). After verifying assumptions of normality and homogeneity of variance, an independent-samples t-test showed no statistically significant differences ((Formula presented)), and the effect size was small (Cohen’s (Formula presented)). Under the conditions evaluated, the dedicated mobile device did not produce measurable gains in knowledge performance compared to the traditional lesson. Longer interventions, larger samples, and longitudinal study designs are needed to clarify when and how this technology may provide educational benefits.
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Model Predictive Control for Visual-Servo Tracking in Unmanned Aerial Vehicles: A Scoping Review
(MDPI AG, 2026-08-31)
Varela Aldas, José
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Toasa, Renato M.
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Moya, Viviana
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Chamorro, William
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Gonzales-Zurita, Oscar
Model predictive control (MPC) has become a central receding-horizon strategy for robotic systems subject to constraints, uncertainty and multiple objectives. In unmanned aerial vehicles (UAVs), its integration with visual servoing makes it possible to formulate tracking problems directly in the image space, keep targets inside the field of view, anticipate vehicle dynamics and coordinate safety, actuation and perception constraints. This scoping review characterizes the available evidence on MPC applied to visual-servo tracking in UAVs. The process identified 20 core studies, 11 strong complementary studies, 12 borderline studies and 18 non-ideal records for the defined scope. The core literature reveals four dominant lines: tracking of targets and visual features, landing or docking on moving platforms, perception-aware control with visibility constraints, and robust, distributed or learning-augmented MPC extensions. The findings show a recent consolidation of methodological directions, although experimental maturity remains uneven across the field, with a predominance of nonlinear MPC, predictive image-based visual servoing, field-of-view constraints and validation in simulation or UAV platforms. Persistent gaps remain in reproducibility, standardized comparison, visual latency, depth uncertainty, outdoor validation and formalization of perceptual costs. © 2026 by the authors.
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INDI: A Low-Cost LLM-Enabled Multimodal Campus Guide Robot
(MDPI AG, 2026-09-03)
Varela Aldas, José
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Carvajal, Christian
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Cadena, Josue
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Del-Valle-Soto, Carolina
University technology campuses contain specialized laboratories, academic programs, and services that can be difficult for first-time visitors to identify. This paper presents INDI, a custom mobile campus guide robot that combines spoken interaction, synthesized speech, touchscreen feedback, animated facial states, head motion, and predefined mobile guidance behaviors. The platform retains the modular mechanical concept of an earlier prototype while replacing its Raspberry Pi and open-loop remote-control architecture with an NVIDIA Jetson Nano, an Arduino Uno motor-control bridge, ROS 1 nodes, encoder feedback, and dual PID speed loops. The robot weighs 2.37 kg, measures (Formula presented.) cm, reaches a software-limited maximum speed of 0.4 m/s, and provides 27 min of continuous operation in the reported tests. Ten repetitions of each motion test produced mean displacements of 1.058 m and 2.182 m for 1 m and 2 m commands, respectively, and mean rotations of 89.2° and 180.3° for 90° and 180° commands. Voice trials achieved 90% correct interaction in a quiet environment and 70% under nearby conversational noise. In an exploratory user study with 13 participants, 16 of 20 assigned tasks were completed and the mean overall rating was 4.31/5. The results demonstrate the feasibility of an integrated, modular, physically embodied information service, while also revealing accumulated linear-motion error, sensitivity to ambient speech, limited battery duration, and the need for grounded institutional knowledge and autonomous localization. These findings are presented as preliminary evidence of technical and interaction feasibility rather than as confirmatory evidence of usability or campus-scale autonomous navigation. © 2026 by the authors.
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Using Predictive Modeling to Assess Social Media's Impact on Parental Well-Being: A Pilot Study
(Springer Nature Switzerland, 2026)
Tutillo, Jimena
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Martinez, Danilo
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Zapata, Mireya
Social media is part of daily life, facilitating communication, entertainment, and immediate access to information. However, it can negatively affect personal well-being and contribute to the development of addictive behavior. The objective of this paper is to examine how social media users in the economically productive age group of 36 to 56 years are affected by these platforms, and how machine learning (ML) and app development can be used to mitigate the impacts of these dopamine-stimulated behaviors. For this purpose, an empirical study using the Social Media Disorder Test showed that 37.5% of the sample had prolonged usage. To aid in the solution, an app was developed that uses machine learning (ML) to classify addiction levels into 8 categories across 3 social media apps. The app monitors usage in real time and issues alerts for excessive consumption patterns to reduce digital dependence. The results show real patterns of moderate addiction among users. It also seeks to raise awareness of social media use so they can set limits and reduce the impact of their use
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Bidirectional immersive digital twin for real-time monitoring and supervisory interaction in PLC-controlled manufacturing
(Springer Science and Business Media LLC, 2026-09-26)
Ortiz, Jessica S.
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Andaluz, Víctor H.
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Carvajal, Christian
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Llamuca, Marlon A.
Digital Twins are increasingly used for monitoring and interaction with automated processes, but integrating immersive environments with active control systems while preserving controller authority remains challenging. This study presents an operational and supervisory immersive Digital Twin for a PLC-controlled automated filling process, enabling bidirectional communication between a physical module and its virtual representation. The architecture integrates a Siemens S7-1200 PLC, OPC UA, MQTT, and a virtual reality environment. PLC-reported process states are mapped to corresponding virtual components, while supervisory commands generated in the virtual environment are returned to the PLC and processed by its control logic before affecting the physical module. Experimental validation was conducted on a laboratory-scale automated bottling module over 100 operating cycles. Process-update latency averaged 78 ms, ranging from 64 to 92 ms, with a system availability of 99.4%. Evaluation of 100 supervisory commands yielded mean command transmission, controlled-process response, and end-to-end round-trip times of 42, 180, and 302 ms, respectively, with a command success rate of 99.0%. An interaction study with 26 participants showed comparable task completion times for WinCC and the immersive Digital Twin, with fewer operational errors, lower NASA–TLX scores, and a System Usability Scale score of 94/100. The results confirm bidirectional supervisory interaction and support physical–digital state correspondence during experimental operation while maintaining PLC control authority. © The Author(s) 2026.

Centro de investigación en Mecatrónica y Sistemas Interactivos

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