Juan Pablo Vasconez Hurtado
Verified @hotmail.com
Faculty of Engineering
Universidad Andrés Bello / Research Professor
Ph.D. in Electronic Engineering from the Universidad Técnica Federico Santa María - Chile. Electronics and Control Engineer from the Escuela Politécnica Nacional (EPN) - Ecuador. Currently, he is a researcher at the Artificial Vision and Intelligence Research Laboratory of the Escuela Politécnica Nacional. He has investigated topics related to Robotics and Artificial Intelligence, Human-Robot Interaction (HRI), Human-Machine Interaction (HMI), assistance and collaborative systems, algorithms able to detect different non-verbal communication techniques such as gestures, actions, and human cognitive parameters. He has also worked in the area of precision agriculture using cooperative robots capable of interacting and sharing the workspace with humans. His areas of interest are robotics, machine learning, deep learning, reinforcement learning, and computer vision.
EDUCATION
Ph.D. in Electronic Engineering - Universidad Técnica Federico Santa María - Chile
Electronics and Control Engineer - Universidad Politécnica Nacional - Ecuador
RESEARCH INTERESTS
Human-robot interaction, artificial intelligence, robotics, electronics.
Scopus Publications
Scopus Publications
Oswaldo Menéndez-Granizo, Alexis Chugá-Portilla, Tito Arevalo-Ramirez, Juan Pablo Vásconez, Fernando Auat-Cheein, and Álvaro Prado-Romo
MDPI AG
Large-scale wireless sensor networks with electric field energy harvesters (EFEHs) offer self-powered, eco-friendly, and scalable crop monitoring in hydroponic greenhouses. However, their practical adoption is limited by the low power density of current EFEHs, which restricts the reliable operation of external sensors. To address this challenge, this work presents a noninvasive EFEH assembled with hydroponic leafy vegetables that harvests electric field energy and estimates plant functional traits directly from the electrical response. The device operates through electrostatic induction produced by an external alternating electric field, which induces surface charge redistribution on the leaf. These charges are conducted through an external load, generating an AC voltage whose amplitude depends on the dielectric properties of the leaf. A low-voltage prototype was designed, built, and evaluated under controlled electric field conditions. Two representative species, Beta vulgaris (chard) and Lactuca sativa (lettuce), were electrically characterized by measuring the open-circuit voltage (VOC) and short-circuit current (ISC) of EFEHs. Three regression models were developed to determine the relationship between foliar moisture content (FMC) and fresh mass with electrical parameters. Empirical results disclose that the plant functional traits are critical predictors of the electrical output of EFEHs, achieving coefficients of determination of R2=0.697 and R2=0.794 for each species, respectively. These findings demonstrate that EFEHs can serve as self-powered, noninvasive indicators of plant physiological state in living leafy vegetable crops.
Piero Vilcapoma, Ingrid Nicole Vásconez, Alvaro Javier Prado, Viviana Moya, and Juan Pablo Vásconez
MDPI AG
Artificial intelligence (AI) is an important tool for improving agricultural tasks. In particular, object detection methods based on convolutional neural networks (CNNs) enable the detection and classification of objects directly in the field. Combined with unmanned aerial vehicles (UAVs, drones), these methods allow efficient crop monitoring. The primary challenge is to develop models that are both accurate and feasible under real-world conditions. This study addresses this challenge by evaluating marigold flower detection using three groups of CNN detectors: canonical models, including YOLOv2, Faster R-CNN, and SSD with their original backbones; modified versions of these detectors using DarkNet-53; and modern architectures, including YOLOv11, YOLOv12, and the RT-DETR. The dataset consisted of 392 images from marigold fields, which were manually labeled and augmented to a total of 940 images. The results showed that YOLOv2 with DarkNet-53 achieved the best performance, with 98.8% mean average precision (mAP) and 97.9% F1-score (F1). SSD and Faster R-CNN also improved, reaching 63.1% and 52.8%, respectively. Modern models obtained strong results: YOLOv11 and YOLOv12 reached 96–97%, and RT-DETR 93.5%. The modification of YOLOv2 allowed this classical detector to compete directly with, and even surpass, recent models. Precision–recall (PR) curves, F1-scores, and complexity analysis confirmed the trade-offs between accuracy and efficiency. These findings demonstrate that while modern detectors are efficient baselines, classical models with updated backbones can still deliver state-of-the-art results for UAV-based crop monitoring.
Jose Manuel Alcayaga, Oswaldo Anibal Menéndez, Miguel Attilio Torres-Torriti, Juan Pablo Vásconez, Tito Arévalo-Ramirez, and Alvaro Javier Prado Romo
MDPI AG
Autonomous navigation in mining environments is challenged by complex wheel–terrain interaction, traction losses caused by slip dynamics, and sensor limitations. This paper investigates the effectiveness of Deep Reinforcement Learning (DRL) techniques for the trajectory tracking control of skid-steer mobile robots operating under terra-mechanical constraints. Four state-of-the-art DRL algorithms, i.e., Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), and Soft Actor–Critic (SAC), are selected to evaluate their ability to generate stable and adaptive control policies under varying environmental conditions. To address the inherent partial observability in real-world navigation, this study presents an original approach that integrates Long Short-Term Memory (LSTM) networks into DRL-based controllers. This allows control agents to retain and leverage temporal dependencies to infer unobservable system states. The developed agents were trained and tested in simulations and then assessed in field experiments under uneven terrain and dynamic model parameter changes that lead to traction losses in mining environments, targeting various trajectory tracking tasks, including lemniscate and squared-type reference trajectories. This contribution strengthens the robustness and adaptability of DRL agents by enabling better generalization of learned policies compared with their baseline counterparts, while also significantly improving trajectory tracking performance. In particular, LSTM-based controllers achieved reductions in tracking errors of 10%, 74%, 21%, and 37% for DDPG-LSTM, PPO-LSTM, TD3-LSTM, and SAC-LSTM, respectively, compared with their non-recurrent counterparts. Furthermore, DDPG-LSTM and TD3-LSTM reduced their control effort through the total variation in control input by 15% and 20% compared with their respective baseline controllers, respectively. Findings from this work provide valuable insights into the role of memory-augmented reinforcement learning for robust motion control in unstructured and high-uncertainty environments.
Piero Vilcapoma, Ivan García, and Juan Pablo Vásconez
Springer Nature Switzerland
Piero Vilcapoma, Diana Parra Meléndez, Ingrid Nicole Vásconez, Gustavo Gatica, and Juan Pablo Vásconez
Springer Nature Switzerland
Pablo Ormeño-Arriagada, Eduardo Navarro, Carla Taramasco, Gustavo Gatica, and Juan Pablo Vásconez
Springer Nature Switzerland
Juan Diego Terneus, Viviana Moya, Faruk Abedrabbo, Juan Pablo Vásconez, and Marcelo Moya
Springer Nature Switzerland
Michael Guerra, Faruk Abedrabbo, Viviana Moya, Angélica Quito, Guillermo Mosquera, and Juan Pablo Vásconez
Springer Nature Switzerland
Viviana Moya, María Maldonado, Juan Pablo Vásconez, Faruk Abedrabbo, David Pozo-Espin, and Marcelo Moya
International Journal of Technology
Victor Osejo, Mateo Ballagán, Estefanía Oñate, Jeffrey Guerrero, Viviana Moya, Andrea Pilco, and Juan Pablo Vásconez
Latin American and Caribbean Consortium of Engineering Institutions
Juan Pablo Vásconez, Julio del Río, Viviana Moya, Andrea Pilco, Inesmar Briseño, Jenny Pantoja, and Oswaldo Menéndez
Springer Nature Switzerland
Mailyn Calderón-Díaz, Ricardo Jiménez, Carolina Saavedra, Juan P. Vásconez, Romina Torres, Miguel A. Solis, Daira Velandia, and Rodrigo Salas
Institute of Electrical and Electronics Engineers (IEEE)
Ivan García, Viviana Moya, Andrea Pilco, Piero Vilcapoma, Leonardo Guevara, Robert Guamán-Rivera, Oswaldo Menéndez, and Juan Pablo Vásconez
Springer Nature Switzerland
J.P. Vásconez, I.N. Vásconez, V. Moya, M.J. Calderón-Díaz, M. Valenzuela, X. Besoain, M. Seeger, and F. Auat Cheein
Elsevier BV
Piero Vilcapoma, Diana Parra Meléndez, Alejandra Fernández, Ingrid Nicole Vásconez, Nicolás Corona Hillmann, Gustavo Gatica, and Juan Pablo Vásconez
MDPI AG
The use of artificial intelligence algorithms (AI) has gained importance for dental applications in recent years. Analyzing AI information from different sensor data such as images or panoramic radiographs (panoramic X-rays) can help to improve medical decisions and achieve early diagnosis of different dental pathologies. In particular, the use of deep learning (DL) techniques based on convolutional neural networks (CNNs) has obtained promising results in dental applications based on images, in which approaches based on classification, detection, and segmentation are being studied with growing interest. However, there are still several challenges to be tackled, such as the data quality and quantity, the variability among categories, and the analysis of the possible bias and variance associated with each dataset distribution. This study aims to compare the performance of three deep learning object detection models—Faster R-CNN, YOLO V2, and SSD—using different ResNet architectures (ResNet-18, ResNet-50, and ResNet-101) as feature extractors for detecting and classifying third molar angles in panoramic X-rays according to Winter’s classification criterion. Each object detection architecture was trained, calibrated, validated, and tested with three different feature extraction CNNs which are ResNet-18, ResNet-50, and ResNet-101, which were the networks that best fit our dataset distribution. Based on such detection networks, we detect four different categories of angles in third molars using panoramic X-rays by using Winter’s classification criterion. This criterion characterizes the third molar’s position relative to the second molar’s longitudinal axis. The detected categories for the third molars are distoangular, vertical, mesioangular, and horizontal. For training, we used a total of 644 panoramic X-rays. The results obtained in the testing dataset reached up to 99% mean average accuracy performance, demonstrating the YOLOV2 obtained higher effectiveness in solving the third molar angle detection problem. These results demonstrate that the use of CNNs for object detection in panoramic radiographs represents a promising solution in dental applications.
Ricardo Paul Urvina, César Leonardo Guevara, Juan Pablo Vásconez, and Alvaro Javier Prado
MDPI AG
This article presents a combined route and path planning strategy to guide Skid–Steer Mobile Robots (SSMRs) in scheduled harvest tasks within expansive crop rows with complex terrain conditions. The proposed strategy integrates: (i) a global planning algorithm based on the Traveling Salesman Problem under the Capacitated Vehicle Routing approach and Optimization Routing (OR-tools from Google) to prioritize harvesting positions by minimum path length, unexplored harvest points, and vehicle payload capacity; and (ii) a local planning strategy using Informed Rapidly-exploring Random Tree (IRRT*) to coordinate scheduled harvesting points while avoiding low-traction terrain obstacles. The global approach generates an ordered queue of harvesting locations, maximizing the crop yield in a workspace map. In the second stage, the IRRT* planner avoids potential obstacles, including farm layout and slippery terrain. The path planning scheme incorporates a traversability model and a motion model of SSMRs to meet kinematic constraints. Experimental results in a generic fruit orchard demonstrate the effectiveness of the proposed strategy. In particular, the IRRT* algorithm outperformed RRT and RRT* with 96.1% and 97.6% smoother paths, respectively. The IRRT* also showed improved navigation efficiency, avoiding obstacles and slippage zones, making it suitable for precision agriculture.
Juan Pablo Vásconez, Elias Schotborgh, Ingrid Nicole Vásconez, Viviana Moya, Andrea Pilco, Oswaldo Menéndez, Robert Guamán-Rivera, and Leonardo Guevara
MDPI AG
Intelligent transportation and advanced mobility techniques focus on helping operators to efficiently manage navigation tasks in smart cities, enhancing cost efficiency, increasing security, and reducing costs. Although this field has seen significant advances in developing large-scale monitoring of smart cities, several challenges persist concerning the practical assignment of delivery personnel to customer orders. To address this issue, we propose an architecture to optimize the task assignment problem for delivery personnel. We propose the use of different cost functions obtained with deterministic and machine learning techniques. In particular, we compared the performance of linear and polynomial regression methods to construct different cost functions represented by matrices with orders and delivery people information. Then, we applied the Hungarian optimization algorithm to solve the assignment problem, which optimally assigns delivery personnel and orders. The results demonstrate that when used to estimate distance information, linear regression can reduce estimation errors by up to 568.52 km (1.51%) for our dataset compared to other methods. In contrast, polynomial regression proves effective in constructing a superior cost function based on time information, reducing estimation errors by up to 17,143.41 min (11.59%) compared to alternative methods. The proposed approach aims to enhance delivery personnel allocation within the delivery sector, thereby optimizing the efficiency of this process.
Oswaldo Menéndez, Juan Villacrés, Alvaro Prado, Juan P. Vásconez, and Fernando Auat-Cheein
MDPI AG
Electric-field energy harvesters (EFEHs) have emerged as a promising technology for harnessing the electric field surrounding energized environments. Current research indicates that EFEHs are closely associated with Tribo-Electric Nano-Generators (TENGs). However, the performance of TENGs in energized environments remains unclear. This work aims to evaluate the performance of TENGs in electric-field energy harvesting applications. For this purpose, TENGs of different sizes, operating in single-electrode mode were conceptualized, assembled, and experimentally tested. Each TENG was mounted on a 1.5 HP single-phase induction motor, operating at nominal parameters of 8 A, 230 V, and 50 Hz. In addition, the contact layer was mounted on a linear motor to control kinematic stimuli. The TENGs successfully induced electric fields and provided satisfactory performance to collect electrostatic charges in fairly variable electric fields. Experimental findings disclosed an approximate increase in energy collection ranging from 1.51% to 10.49% when utilizing TENGs compared to simple EFEHs. The observed correlation between power density and electric field highlights TENGs as a more efficient energy source in electrified environments compared to EFEHs, thereby contributing to the ongoing research objectives of the authors.
Viviana Moya, Angélica Quito, Andrea Pilco, Juan P. Vásconez, and Christian Vargas
Ital Publication
In recent years, the accurate identification of chili maturity stages has become essential for optimizing cultivation processes. Conventional methodologies, primarily reliant on manual assessments or rudimentary detection systems, often fall short of reflecting the plant’s natural environment, leading to inefficiencies and prolonged harvest periods. Such methods may be imprecise and time-consuming. With the rise of computer vision and pattern recognition technologies, new opportunities in image recognition have emerged, offering solutions to these challenges. This research proposes an affordable solution for object detection and classification, specifically through version 5 of the You Only Look Once (YOLOv5) model, to determine the location and maturity state of rocoto chili peppers cultivated in Ecuador. To enhance the model’s efficacy, we introduce a novel dataset comprising images of chili peppers in their authentic states, spanning both immature and mature stages, all while preserving their natural settings and potential environmental impediments. This methodology ensures that the dataset closely replicates real-world conditions encountered by a detection system. Upon testing the model with this dataset, it achieved an accuracy of 99.99% for the classification task and an 84% accuracy rate for the detection of the crops. These promising outcomes highlight the model’s potential, indicating a game-changing technique for chili small-scale farmers, especially in Ecuador, with prospects for broader applications in agriculture. Doi: 10.28991/ESJ-2024-08-02-08 Full Text: PDF
Juan Sebastian Estrada, Juan Pablo Vasconez, Longsheng Fu, and Fernando Auat Cheein
Elsevier BV
Andrea Pilco, Viviana Moya, Angélica Quito, Juan P. Vásconez, and Matías Limaico
EJournal Publishing
This study sheds light on the evolution of the agricultural industry and highlights advances in production area. The salient recognition of fruit size and shape as critical quality parameters underscores the significance of the research. In response to this challenge, the research introduces specialized image processing techniques designed to streamline the sorting of apples in agricultural settings, specifically emphasizing accurate apple width estimation. A purpose-built machine was designed, featuring an enclosure box housing a cost-effective camera for the vision system and a chain conveyor for classifying Malus domestica Borkh kind apples. These goals were successfully achieved by implementing image preprocessing, segmentation, and measurement techniques to facilitate sorting. The proposed methodology classifies apples into three distinct classes, attaining an impressive accuracy of 94% in Class 1, 92% in Class 2, and 86% in Class 3. This represents an efficient and economical solution for apple classification and size estimation, promising substantial enhancements to sorting processes and pushing the boundaries of automation in the agricultural sector.
Oswaldo Menendez, Felipe Ruiz, Daniel Pesantez, Juan Vasconez, and Jose Rodriguez
IEEE
This work introduces a current control strategy for Voltage Source Inverters (VSI) using data-driven control systems, particularly employing a framework based on Deep Reinforcement Learning agents. Unlike the other techniques in the literature, we have avoided using a modulator by including a Deep Q-Network agent. In addition, an analysis of the impact of different Deep Neural Network (DNN) architectures on control system performance, specifically considering the number of layers and neurons, is presented. To this end, different DQN agents were designed, trained, and tested. Also, a two-level voltage source power inverter is simulated to validate the proposed data-driven control based on DQN agents. The performance of the control strategy is analyzed in terms of computational cost, Root Mean Square Error (RMSE), and Total Harmonic Distortion (THD). Simulated results reveal that the proposed control strategy performs strongly in the current control, with a maximum RMSE of 0.83 A and a THD of $5.29 \\%$ at a 10 kHz sampling frequency when a DNN with one layer and five neurons is used.
Juan Pablo Vásconez, Mailyn Calderón-Díaz, Inesmar C. Briceño, Jenny M. Pantoja, and Patricio J. Cruz
Springer Nature Switzerland
Juan I. Saez Rojas, Jenny M. Pantoja, Mónica Matamala, Inesmar C. Briceño, Juan Pablo Vásconez, and Alfonso R. Romero-Conrado
Elsevier BV