A specific challenge in robotic control applications is the identification and regulation of actuators that provide mechanical traction and motion to the robot links. The design of actuator control laws, grounded in parametric identification and experimental motor characterization, enables numerical simulations to explore diverse operating scenarios. This article presents the initial phases in the development of a robotic rehabilitation system, focused on the kinematic modeling of a parallelogram-configuration robot for upper-limb therapy, the fuzzy identification of its actuators, and their closed-loop evaluation using a fuzzy Parallel Distributed Compensation (PDC) controller with state feedback (Ackermann), whose poles are optimized via the Grey Wolf Optimizer (GWO) metaheuristic. This controller was selected for its congruence with the nonlinear universe of discourse defined by the identified model, a key feature for operation within specific functional ranges in medical applications. The simulation and hardware platform results provide evidence that fuzzy dynamic models constitute a valuable tool for application in rehabilitation systems. This work serves as a foundation for future physical implementations with the fully coupled robotic system, in order to ensure operational safety prior to the start of clinical trials.

Precision agriculture is a key technology for addressing challenges such as increasing food demand, labour shortages, and the environmental impact of intensive agrochemical use. In this context, selective weed management remains a critical issue due to its direct effect on crop productivity and sustainability. This article presents a simulation-based framework for the design and evaluation of an agricultural robotic module for the detection, classification, and selective intervention of weeds. The proposed system integrates convolutional neural networks and the kinematic model of a 2DOF robot manipulator with 5 links for weed classification and treatment. The system is evaluated in a virtual environment, where camera calibration, perception accuracy, and the performance of the kinematic model are analysed. Quantitative results include detection accuracy, localization error, and intervention success rate under simulated field conditions. The results demonstrate selective weed management and the feasibility of simulation for developing weed control systems, while also identifying the main challenges for real-world deployment.

Se presenta el diseño mecánico de una máquina herramienta de Control Numérico Computarizado (CNC) de 5 ejes para aprendizaje y uso en centros de maquinado. En la actualidad, el uso de máquinas CNC en la industria se vuelve popular, y el uso de centros de maquinado de 5 ejes se encuentra en crecimiento por su capacidad de maquinar piezas de mayor calidad y complejidad; además de reducir los tiempos de maquinado. Entonces, surge la necesidad en universidad, centro de investigación y desarrollo tecnológico, que formen profesionalmente estudiantes en el manejo, operación, diseño y desarrollo de equipos de maquinado de 5 ejes. De estos puntos, se tiene problemas en el desarrollo de equipos, la información que existe es mayormente de adaptaciones de máquinas de 3 ejes. Buscando solución, se presenta el diseño mecánico, con cálculos relativos para determinar las fuerzas de corte, hacer análisis de elemento finito, y determinar los torques necesarios en cada uno de los ejes de la máquina.

This chapter presents different research on the application of Artificial Intelligence (AI) in security surveillance and emergency response. Artificial Intelligence is considered a technological advancement, which has been used for social good, in the security approach is a sustainable development goal proposed by the United Nations (UN). In this research so far it has been found that from 2015 to date artificial intelligence has been applied in advancing security and surveillance through automatic facial recognition, intrusion detection, real-time data analysis for threat detection, intelligent video surveillance projects have also been developed, AI has been integrated in emergency response, in incident forecasting and prevention systems, it has been applied in incident detection as well as in the allocation and dispatch of resources, and among other projects are the allocation of resources, relocation, emergency response and medical care in disaster and natural catastrophe situations.

This article presents the conceptual design of a robotic rehabilitation device focused on the recovery of the elbow joint, aimed primarily at individuals who have suffered a cerebrovascular disease or trauma to that joint. The device was developed following Ullman’s mechanical design methodology, which allowed the project to be structured systematically. The robot enables repetitive flexion-extension movements of the elbow, with the goal of restoring mobility to the affected upper limb. To ensure the mechanical integrity and functional reliability of the proposed system, a structural validation was conducted through finite element analysis, along with studies for the selection and design of the electronic and instrumentation components. This development represents a step forward toward accessible, functional, and efficient rehabilitation solutions.

The integration of Artificial Intelligence (AI) into healthcare has open a new era of medical diagnosis and treatment. This chapter critically examines a collection of scholarly articles that delve into the multifaceted applications of AI in the healthcare domain such as diagnosis using image and chemical studies, patient familiar history and big data acquired through the use of wearables. Also, we dive into the ethical and legal aspects of training IA with private data and which repercussions can have.