Chicaiza Claudio, Fernando

Preferred name

Main Affiliation

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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Delayed Bilateral Teleoperation of Mobile Manipulators With Hybrid Mapping: Rate/Nonlinear-Position Modes
(2024)
Chicaiza Claudio, Fernando
;
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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A Voice-Controlled Robotic System Using ChatGPT for Intuitive Human-Robot Interaction
(2026)
Chicaiza Claudio, Fernando
Industry 5.0 emphasizes the human-centered integration of advanced technologies within production systems, promoting adaptive and collaborative frameworks where humans remain central to system intelligence and decision-making. This work presents a natural language, voice-controlled framework that leverages ChatGPT to interpret spoken commands and facilitate intuitive control of a mobile robot simulated in MATLAB. The architecture integrates speech-to-text conversion, context-aware reasoning through generative AI, and real-time bidirectional communication between Python (ChatGPT) and MATLAB via a feedback loop. To evaluate the system’s effectiveness, three user-centered experiments were conducted with twelve participants issuing natural language commands under increasing task complexity. The experiments demonstrated the system’s ability to accurately interpret user intent and execute corresponding robot behaviors. User performance and experience were assessed using the NASA Task Load Index (NASA-TLX), revealing high scores in confidence and perceived performance, along with low ratings in effort, mental, and physical demand. Nonetheless, moderate frustration and temporal demand scores were observed, primarily due to response delays in voice processing. The third experiment, which involved the most complex navigation scenario, yielded the highest performance ratings, underscoring the system’s potential in challenging environments. Overall, the results highlight the framework’s usability and promise for real-world human-robot interaction and collaborative applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
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Dual-Arm Mobile Manipulator Teleoperation With Coupled Rate-Position Mapping Under Time-Varying Delays
(2025)
Chicaiza Claudio, Fernando
;
Emanuel Slawiñski
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Vicente Mut
Bilateral teleoperation of dual-arm mobile manipulators presents considerable challenges. These include increased kinematic redundancy, coordination complexity, and sensitivity to time-varying communication delays. Most existing approaches control each arm independently using leader position-based mappings; however, the teleoperation of mobile dual-arm systems mechanically coupled through an articulated torso remains largely unexplored. Unlike previous studies that primarily emphasize controller tuning, this work emphasizes the critical role of command mapping design in achieving effective teleoperation. Specifically, it analyzes a coupled rate/nonlinear-position mapping strategy that enables coordinated motion in a torso-equipped dual-arm mobile manipulator operated through a dual-leader haptic interface. The proposed framework extends existing single-arm teleoperation schemes by introducing a coupled reference generation mechanism, where the reference for the secondary arm depends on both the position of the secondary leader and the rate-position-type reference of the primary arm. A two-stage stability analysis, based on the Lyapunov-Krasovskii criterion and numerical simulations, is conducted to determine the parameter conditions required to ensure bounded coordination errors in the presence of time-varying communication delays. Preliminary human-in-the-loop tests in dual-arm pick-and-place tasks support the theoretical findings and demonstrate a clear dependence of motion stability on the structure of the command mapping. The results provide foundational insights into the joint optimization of control and mapping strategies and offer practical guidelines for advancing teleoperation in complex, real-world scenarios.
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Experimental Validation of a Kinematic Control Strategy for Trajectory Tracking in Quadruped Robots
(2025)
Carvajal, Christian
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Varela Aldas, José
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Víctor H. Andaluz
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Chicaiza Claudio, Fernando
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Ayala-Chauvin, Manuel Ignacio
This work presents a high-level control architecture for trajectory tracking in quadruped robots. The proposed controller is based on the motion kinematics of the robot's center of mass (CoM). The proposed strategy transforms planned trajectories in Cartesian space into motion velocity commands for the robot, using a differential kinematic model that relates the velocity of the robot's operational point to its velocity in the XY-plane. The control scheme is organized hierarchically, where the kinematic controller operates independently from the system dynamics, which are handled by low-level controllers. The proposed control architecture is experimentally validated using the Unitree Go2 quadruped robot, employing MATLAB and ROS2 tools. The results confirm the feasibility of using purely kinematic models for high-level locomotion task control under real-world operating conditions. © 2025 IEEE.
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Tomato classification with YOLOv8: Enhancing automated sorting and quality assessment
(2025)
Viviana Moya
;
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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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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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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Neural networks meet PID control: Revolutionizing manipulator regulation with gravitational compensation
(2025)
Marco Moran-Armenta
;
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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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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Chicaiza Claudio, Fernando

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