Chicaiza Claudio, Fernando

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Centro de investigación en Mecatrónica y Sistemas Interactivos

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IoT Architecture Prototype for Real-Time Monitoring and Control in a Simulated Automation Environment
(Springer Nature Switzerland, 2026)
Chuchico, Cristian P.
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Semanate, Clinton
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Chicaiza Claudio, Fernando
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Pilatásig, Marco
The Internet of Things (IoT) has become a fundamental element for digital transformation in industry, boosting efficiency, security, and innovation. This paper presents an IoT architecture designed for real-time monitoring and control of a virtual level station, addressing critical challenges in industrial automation. The level process is simulated using Factory IO and regulated by a control algorithm developed in TIA Portal V16, executed via PLC Sim Advanced. Data is acquired by Node-RED from the PLC and transmitted to an MQTT broker, while a dashboard hosted on Ubidots enables remote access. Historical data of key parameters is recorded to facilitate detailed analysis of process behavior. Its flexible design allows the simulated process to be easily replaced or expanded with additional sensors and parallel processes, providing significant scalability for future applications. Results show a 55ms response time, data transmission with less than 100ms latency (PLC to cloud), and system integrity during network failures. Based on the Industrial Internet Reference Architecture (IIRA), this system provides a replicable framework for Industry 4.0 implementations, particularly in: Digital twin development, safe operator training, and cost-effective modernization of existing systems. © 2026, Springer Science and Business Media Deutschland GmbH. All rights reserved.
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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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Lyapunov-Stable Neural Network Control for Position and Force Coordination in Delayed Bilateral Teleoperation Systems
(Institute of Electrical and Electronics Engineers (IEEE), 2026)
Slawiñski, Emanuel
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Chicaiza Claudio, Fernando
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Rossomando, Francisco G.
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Mut, Vicente
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Moreno-Valenzuela, Javier
Stability and transparency are intrinsically coupled and must be addressed simultaneously in delayed bilateral teleoperation systems, where there is a fundamental tradeoff between ensuring closed-loop stability and achieving accurate force–position coordination. This challenge becomes particularly critical when interacting with remote environments under time-varying communication delays, since the human operator introduces nonlinear, time-varying, and often unpredictable dynamics into the closed-loop system. This article presents an adaptive neural-network (NN)-based compensation strategy embedded within a model-based control framework to enhance dual coordination in bilateral teleoperation. By combining classical control design with online NN adaptation, the proposed controller compensates for parametric uncertainties, unmodeled dynamics, interaction forces, and communication delays without requiring explicit models of the human operator or the remote environment. The adaptive structure enables real-time learning of unknown nonlinearities while preserving the stability guarantees provided by the underlying control architecture. A theoretical analysis of the closed-loop teleoperation system is developed, demonstrating stability in the presence of human-applied forces, environment interaction forces, and time-varying communication delays. Numerical simulations conducted on two-degrees-of-freedom manipulators validate the feasibility and practical viability of the proposed approach. Different explicit models of the human operator are considered in simulation to assess the sensitivity to operator dynamics. The results show that a bounded dual coordination of force and position is achieved under delayed communication regardless of the assumed operator model, supporting the potential application of the method in real-world teleoperation scenarios.
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The Scalability Trap: A Systematic Review of Mobility Constraints in Mid-size Unmanned Ground Vehicles
(Springer Science and Business Media LLC, 2026-09-10)
Chicaiza Claudio, Fernando
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Chuchico Arcos, Cristian Paul
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ROMERO MORALES , ESTALIN JOSE
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Moreno-Vega, Esteban
The deployment of Unmanned Ground Vehicles in unstructured environments is currently polarized between lightweight scouting platforms and heavy industrial machinery, leaving a significant void in the functional capabilities of mid-sized robotic systems. This systematic review investigates the architectural constraints of the mid-size category, strictly defined in this work by a total Gross Vehicle Weight (GVW) ranging from 100 to 1000 kg through a Comparative Boundary Analysis of 24 empirical studies. By stratifying the literature into lightweight, mid-size, and heavy-duty clusters, the analysis identifies a Scalability Trap where kinematic principles effective at small scales become energetically prohibitive when upscaled. Specifically, the review demonstrates that the resistive turning moment inherent to skid-steering architectures scales linearly with mass, rendering differential drive mechanisms increasingly inefficient and prone to immobilization as payload scales in highly deformable soils. Furthermore, the synthesis of suspension dynamics reveals that rigid chassis designs in this intermediate class struggle to manage dynamic vertical forces, leading to high-frequency sensor noise and intermittent traction loss that pneumatic tyre compliance cannot mitigate. Algorithmically, the review establishes that reactive kinematic controllers frequently diverge under the inertial non-linearities of this mass regime during high-speed or transient maneuvers, necessitating predictive dynamic frameworks capable of explicitly managing actuator saturation and stochastic wheel slip. Despite these architectural and algorithmic limitations, empirical evidence confirms the agronomic superiority of the mid-size class, which delivers measurable crop yield improvements relative to conventional tractors by preserving soil porosity through reduced ground pressure. Consequently, this study concludes that the future of field robotics depends on shifting from rigid, skid-steered prototypes to morphologically adaptive systems utilizing 4-Wheel Steering, passive articulation, and predictive traction control to unlock the operational potential of the mid-size sector. © The Author(s) 2026.
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Neural networks meet PID control: Revolutionizing manipulator regulation with gravitational compensation
(2025)
Marco Moran-Armenta
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Jorge Montoya-Cháirez
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Francisco G. Rossomando
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Emanuel Slawiñski
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Vicente Mut
This research proposes an innovative approach to improve the performance of regulation control systems in manipulators by combining PID control with gravitational compensation using neural networks. In this work, a modified PID control structure that incorporates a gravitational compensation term given by a neural network is introduced, thus allowing a more precise and adaptive response to gravitational and dynamic perturbations of the system. Furthermore, the controller's performance is evaluated through real-time experiments in two manipulators, comparing its performance with the same structure, one without integral action, another without neural compensation and the last one assuming that the gravity vector is known. The results show a significant improvement in system regulation accuracy, demonstrating the proposed controller's effectiveness.
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Delayed Bilateral Teleoperation of Mobile Manipulators With Hybrid Mapping: Rate/Nonlinear-Position Modes
(2024)
Chicaiza Claudio, Fernando
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EMANUEL SLAWIñSKI
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VICENTE MUT
Mobile manipulators find versatile applications across various fields, leveraging the combination of autonomous functionalities and bilateral teleoperation schemes to enhance the effectiveness of these robotic mechanisms. Regarding teleoperation, command generation involves a leader robot with a few degrees of freedom in a bounded workspace, accompanied by a redundant follower robot operating in an unbounded workspace. This article introduces the concept of Cartesian/articular control for delayed bilateral teleoperation of a mobile manipulator, where the follower robot aims to execute the rate/nonlinear-position commands issued by a human handling the leader robot through a proposed hybrid mapping. We implement a P+d controller applied in Cartesian space for the leader while a controller based on inverse kinematics in joint space is employed for the follower, taking advantage of its redundancy. We then propose a Lyapunov-Krasovskii candidate function to analyze theoretically and numerically the time derivative of the functional on the system trajectories. As a result, we derive the conditions that the proposed hybrid mapping and controller parameters must satisfy to ensure bounded errors. Finally, we statistically evaluated objective metrics from multiple pick-and-place task executions considering time delays to quantify the performance achieved
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Tomato classification with YOLOv8: Enhancing automated sorting and quality assessment
(2025)
Viviana Moya
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Michael Guerra
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Karina Pazmiño
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Faruk Abedrabbo
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Chicaiza Claudio, Fernando
This study presents the design and implementation of an automated system for sorting and measuring kidney tomatoes using a YOLOv8 model with a size estimation algorithm. The proposed system integrates computer vision and deep learning with a physical sorting mechanism to categorize tomatoes into three classes: green, red, and damaged, while also determining their size. The classification model was trained on a dataset of 2,145 images of tomatoes taken from different sources and lighting conditions to enhance performance during training. The implemented prototype consists of a conveyor belt equipped with sensors and a high-resolution camera to capture and analyse tomato characteristics in real-time. A servo-driven sorting mechanism then directs the classified tomatoes into their respective bins. Experimental validation and testing show that the model achieves a classification accuracy of 99.6% and a size estimation accuracy of 97.1%, aiding in a reliable and efficient post-harvest sorting process. The proposed system not only reduces the probability of human error but also improves the precision of tomato classification. Future developments will focus on refining and adapting existing AI methodologies to improve their effectiveness in agricultural environments. This includes enhancing model robustness, improving classification accuracy under real-world conditions, and tailoring AI tools to better meet the demands of industrial tomato sorting
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Robust Visual Servoing for Quadruped Robots: ISS-Based Target Tracking With Secondary Formation Objectives
(2026)
Carvajal, Christian
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Chicaiza Claudio, Fernando
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Varela Aldas, José
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Víctor H. Andaluz
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Flavio Roberti
This workpresents the design, implementation, and experimental validation of a visual servo control scheme formulated at the task-space level and applied to a quadruped robot equipped with an active two degree of freedom (2DoF) vision system for continuous tracking of moving targets. The proposed controller is based on a differential kinematic model that maps the robot's task-space velocity references to the kinematic evolution of the active vision system, incorporating a target velocity estimator that enables dynamic compensation of the target motion projected onto the image plane. The control architecture exploits the kinematic redundancy of the robotic system to simultaneously address multiple control objectives within a hierarchical framework. The primary task ensures target centering in the image plane, thereby maintaining visibility within the robot's field of view, while the secondary task regulates relative formation by controlling the distance and orientation between the robot and the target. Quadruped locomotion is handled by a low-level gait controller and is not modified by the proposed scheme. Closed-loop stability is rigorously analyzed using Lyapunov theory, demonstrating Input-to-State Stability properties in the presence of bounded disturbances. Simulation and real-world experimental results obtained with the Unitree Go2 robot confirm the robustness and stability of the proposed approach for dynamic visual tracking in realistic scenarios. © 2020 IEEE.
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Web-based pulmonary telehabilitation: a systematic review
(2024)
Ayala-Chauvin, Manuel Ignacio
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Chicaiza Claudio, Fernando
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Patricia Acosta-Vargas
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Jadán Guerrero, Janio
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Verónica Maldonado-Garcés
Web-based pulmonary telerehabilitation (WBPTR) can serve as a valuable tool when access to conventional care is limited. This review assesses a series of studies that explore pulmonary telerehabilitation programmes delivered via web-based platforms. The studies involved participants with moderate to severe chronic obstructive pulmonary disease (COPD). Of the 3190 participants, 1697 engaged in WBPTR platforms, while the remaining 1493 comprised the control groups. Sixteen studies were included in the meta-analysis. Web-based pulmonary telerehabilitation led to an increase in daily step count (MD 446.66, 95% CI 96.47 to 796.86), though this did not meet the minimum clinically important difference. Additionally, WBPTR did not yield significant improvements in the six-minute walking test (MD 5.01, 95% CI − 5.19 to 15.21), health-related quality of life as measured by the St. George’s Respiratory Questionnaire (MD − 0.15, 95% CI − 2.24 to 1.95), or the Chronic Respiratory Disease Questionnaire (MD 0.17, 95% CI − 0.13 to 0.46). Moreover, there was no significant improvement in dyspnoea-related health status, as assessed by the Chronic Respiratory Disease Questionnaire (MD − 0.01, 95% CI − 0.29 to 0.27) or the modified Medical Research Council Dyspnoea Scale (MD − 0.14, 95% CI − 0.43 to 0.14). Based on these findings, this review concludes that WBPTR does not offer substantial advantages over traditional care. While slight improvements in exercise performance were observed, no meaningful enhancements were noted in dyspnoea or quality of life metrics. Overall, WBPTR remains a complementary and accessible option for managing and monitoring COPD patients. However, further research and innovation are required to improve its efficacy and adapt it to various clinical environments.
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Automated Mouth State Recognition for Robotic Feeding Assistance
(2025)
Samuel Peña
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Viviana Moya
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Chicaiza Claudio, Fernando
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Danilo Chávez
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Juan Pablo Vásconez
Feeding poses a significant challenge for patients with limited upper limb mobility. A robotic assistant equipped with the capability to detect a patient's mouth and deposit food can effectively support the user during feeding. Since each patient exhibits unique physical characteristics, it is essential to develop an automated system capable of accurately identifying the mouth's location. This allows the definition of a precise three-dimensional target point where a robotic manipulator can deliver food. Although various techniques are available for facial feature detection, some demonstrate notable advantages in specific applications. Considering the constraints of limited processing capacity, we propose the use of Facemesh to identify the patient's point of interest, specifically the mouth. This technique enables the determination of the precise location for food delivery. To ensure the robot can reach both the patient's mouth and the food container, an inverse kinematics-based controller is implemented. The system's performance is evaluated, demonstrating the effectiveness of integrating the patient into the control loop for seamless operation.
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Chicaiza Claudio, Fernando

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