Juan Pablo Vásconez

This study presents the development and validation of a real-time optical character recognition (OCR) system for identity verification in residential access control scenarios, specifically evaluating the effects of environmental variability and user interaction on system performance. The proposed methodology integrates a U-Net architecture with a ResNet-34 encoder for semantic document segmentation and utilizes Tesseract OCR for digit recognition, deployed on an embedded platform. Experimental evaluations were performed under diverse lighting conditions, varying camera-to-document distances, and differing document physical states, followed by field validation within a gated community. The system demonstrated optimal segmentation performance with a mean Intersection over Union (IoU) of 0.82 and a digit recognition accuracy of 87.5% under high-illumination conditions at distances ranging from 7 to 30 cm. Furthermore, system usability and user workload were assessed through standardized psychometric instruments, including the System Usability Scale (SUS), the User Experience Questionnaire (UEQ), task-efficiency metrics, and the NASA Task Load Index (NASA-TLX). Results indicated high perceived usability (SUS = 86.5) coupled with moderate cognitive demand. The findings demonstrate that while environmental factors significantly influence algorithmic performance, user interaction critically impacts overall system reliability. The novelty of this research lies in integrating technical performance metrics with human-centered analysis in a real-world deployment.

Affective computing has become increasingly relevant in child–robot interaction (CRI), particularly in social robotics, emotion recognition, engagement assessment, and autism-related interventions. This systematic review with a critical and integrative synthesis analyzes 105 included studies to examine how affect is sensed, represented, processed, expressed, and evaluated in CRI. The literature search was conducted in IEEE Xplore, Web of Science, Scopus, and PubMed, following a systematic screening process guided by the review objectives. A descriptive and structured narrative synthesis was conducted considering publication characteristics, robot platform and morphology, target population, sensing modalities and observed affect-relevant features, affective constructs and representation models, computational and control mechanisms, robot affective expression, evaluation strategies, and remaining research gaps. The findings show a strong emphasis on ASD-related contexts, visually observable and behavioral features, facial emotion recognition, body movement analysis, and engagement assessment. The review also identifies important limitations, including reliance on camera-based affect recognition, comparatively limited use of physiological and other complementary sensing modalities, unclear alignment between robot roles and interaction strategies, insufficient reporting of robot emotional expressiveness and control mechanisms, and limited attention to explainability, data governance, and long-term ethical implications. Based on these findings, an integrative taxonomy of affective computing in CRI is proposed, comprising six interconnected dimensions: interaction context; sensing modalities and observed features; affective constructs and representation models; computational and control mechanisms; robot affective expression; and evaluation and adaptation strategies. Rather than treating these dimensions as entirely novel categories, the taxonomy consolidates and extends previously fragmented classifications into a child-centered representation of the affective interaction process. Overall, this review argues that affective CRI should move beyond automatic emotion recognition toward multimodal, embodied, developmentally appropriate, explainable, and ethically grounded robot interaction.

Agricultural human robot collaboration requires perception systems that interpret both ongoing activity and the broader work conditions in which robotic assistance may be required. This work proposes a visual semantic framework for predicting three robot assistance categories, None, Material Handling, and Harvesting, from RGB images of agricultural work environments. The framework combines scene classification and object perception with a structured semantic representation integrating contextual, object level, segmentation derived, spatial, workload related, and higher level information. This representation captures complementary evidence from the observed agricultural scene. Scene classification was evaluated using ResNet50, EfficientNet B0, ViT B/16, and Swin Tiny, while canonical, lightweight modified, and modern detection and segmentation architectures were compared for object perception. ResNet50 was retained as the scene classifier, achieving a test Macro F1 of 84.31%, whereas YOLO11n Seg was selected for object perception with test box and mask mAP@0.5 values of 88.9% and 88.3%, respectively. For assistance prediction, five machine learning classifiers were evaluated across eight feature configurations. Bagging combined with the 64 feature Pepper harvest focus representation achieved the highest validation Macro F1 of 91.87%, improving the Scene only configuration by 10.35 percentage points and slightly outperforming the complete 77 feature representation. After validation based selection and train only refitting, the frozen model achieved 80.56% Accuracy, 80.06% Macro F1, and 80.94% Weighted F1 on the held out test subset. These results show that activity context is informative, while task oriented object, spatial, and semantic cues provide a richer basis for assistance prediction than scene context alone.

Reinforcement learning (RL) has become an effective paradigm for enabling autonomous robots to acquire navigation policies directly from interaction with complex and uncertain environments. Nevertheless, autonomous path planning for Skid-Steer Mobile Manipulators (SSMMs) remains a challenging problem because it requires the coordinated control of the non-holonomic mobile base and the manipulator while simultaneously accounting for obstacle avoidance and wheel–terrain interaction effects. This paper presents and evaluates RL-based path planning strategies for SSMMs, explicitly incorporating coupled dynamics of the mobile platform and manipulator to generate collision-free trajectories under varying terrain conditions. The proposed framework incorporates a slip-aware reward formulation that penalizes discrepancies between commanded and measured robot motion while accounting for longitudinal and lateral slip resulting from wheel–terrain interaction. The main contributions are (i) a unified RL-based framework based on actor–critic techniques for SSMM path planning, integrating the mobile base and manipulator dynamics within a coupled system representation; (ii) a physics-aware multi-objective reward formulation that incorporates wheel–terrain interaction into policy learning; and (iii) the implementation via simulation and field validation of the proposed policies under progressively complex navigation conditions and real underground mining scenarios. The framework is evaluated using four RL algorithms across multiple environments and maps from real mining scenarios, encompassing diverse navigation conditions and start-to-goal configurations. The evaluated methods include Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO), Soft Actor–Critic (SAC), and Twin Delayed DDPG (TD3). Experimental field results show that SAC achieves the lowest planning time, reducing the planning time by 127.3%, 24.1%, and 2.52% compared with PPO, TD3, and DDPG, respectively. SAC also achieves the shortest path, reducing the average path length by 20.32%, 8.58%, and 1.90% compared with PPO, DDPG, and TD3, respectively. Moreover, SAC generates smoother control profiles for both the mobile base and the manipulator arm, while TD3 exhibits competitive performance across several navigation metrics. The proposed framework demonstrates the potential of slip-aware RL for coordinated SSMM navigation, providing a practical foundation for improving the safety, energy efficiency, and operational autonomy of mobile manipulators exposed to complex mining environments.

The deployment of natural-language-to-SQL (NL-to-SQL) systems in primary healthcare requires more than accurate query generation: it also requires governed data access, robustness to local terminology, and reliable handling of ambiguous user requests. This study evaluated a pilot proof-of-concept integrating a Spanish-language NL-to-SQL assistant with a governed, read-only outpatient scheduling repository derived from the Rayen information system used in a Centro de Salud Familiar (CESFAM) setting in Renca, Chile. The data used by the prototype were accessed through an external company responsible for data management in this context. The prototype was implemented with MindsDB as an artificial intelligence (AI)-enabled database layer and operated on anonymized, delayed secondary scheduling data. Evaluation was conducted through a Slack interface using 252 audited interactions from 42 users, with six assigned interactions per user and up to three exchanges per interaction. SQL correctness reached 240/252 (95.2%), whereas both query correctness and answer correctness reached 144/252 (57.1%). These findings suggest that governed pilot deployment for outpatient schedule monitoring may be feasible under controlled institutional conditions, while indicating that the main remaining barriers are semantic rather than purely syntactic, specifically ambiguity handling, institution-specific operational language, and faithful answer verbalization. The study therefore contributes deployment-oriented pilot evidence and clarifies where operational Spanish NL-to-SQL remains fragile under real institutional constraints.