Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Metamaterial-enhanced soft pneumatic actuators: tailoring stiffness and deformation modes(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06-09) Ochoa Sánchez, Oscar; González de Alba, Alejandro; emipsanchez; Cuan Urquizo, Enrique; Pérez Santiago, Rogelio; School of Engineering and Sciences; Campus Monterrey; Sandoval Castro, Xochitl YamileSoft robotics is redefining robotic systems by leveraging compliant materials, with soft actuators serving as key enablers of motion and functionality. Despite current strategies for designing soft pneumatic actuators, challenges remain in achieving tunable stiffness and deformation, limiting their versatility and reusability. This thesis explores a novel approach: integrating metamaterials as structural reinforcements to enable programmable deformation modes and localized stiffness control. To achieve this, functionally graded metamaterial beams are designed through a novel inverse method and incorporated into bending actuators. The study systematically evaluates the fabrication of reconfigurable actuators, the impact of varied reinforcements on stiffness and deformation, a constant curvature-based forward kinematic model, and an application case of a gripper capable of handling objects of different sizes and shapes. Additionally, metamaterial reinforcements are applied to origami-inspired contraction actuators, facilitating programmable motion for applications such as parallel platforms and robotic arms. By combining experimental, computational, and analytical methods, this research validates a novel strategy for the development of tunable soft actuators. The results demonstrate the ability to alter conventional deformation patterns, enabling unexpected capabilities such as inducing torsion in bending actuators and bending in contraction actuators. This work underscores the transformative potential of metamaterials in advancing reconfigurable and programmable soft actuators with tailored mechanical properties, paving the way for complex, real-world applications.
- Data-driven control of a five-bar parallel robot with compliant joints(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-09-01) Ramírez Martínez, Angel; Chong Quero, Jesús Enrique; emimmayorquin; Cruz Villar, Carlos Alberto; Escuela de Ingeniría y Ciencias; Cervantes Culebro, HéctorThis thesis presents a data-driven approach control to minimize trajectory tracking error of a five-bar robot with compliant joints. Adding compliance in the joints introduces modeling and control challenges due to the flexibility. Traditional model-based methods rely on accurate analytical models of the dynamics, which are difficult to obtain for compliant systems. This motivates a data-driven technique that learns online and offline model directly from experiments using vision-based measurements on the physical robot. The core methodology is divided in online training and offline training. The data-driven controller with online training bases its operation on letting the dynamics of the system run using a controller/compensator, in this case it is used a PID. The online training is based on a Neural Network whose input is the Cartesian position of the end-effector, obtained by the vision-based motion capture system. The Neural Network output is the control signal therefore approximates the inverse dynamic model. With this, it is possible to enhanced the control law of the robot to do tracking error minimization. The offline approach involves collecting time-series data capturing the robot's end-effector Cartesian position while moving in its available workspace. The Cartesian position is also obtained by the vision-based motion capture system. These data, which encapsulate the impact of the compliant joints, are used to train a Neural Network to represent the forward dynamics model. The network maps current state and control law inputs to predictions of the next state. Once trained, this Neural Network model is used by an implementation of Model Predictive Control framework to optimize control laws of the two motors to minimize tracking error of a desired end-effector trajectory. At each control step, a finite horizon optimal control problem is solved to find the control signals that minimizes tracking error over a future window. The Neural Network dynamics model is used to predict the outcomes resulting from candidate control actions. Solving this optimization in receding horizon fashion provides feedback correction to reject disturbances. Online training allows the controller to continuously learn from new data, but it relies on the controller used in order to approximate the dynamic system. Nevertheless, online training requires less compute resources and only one thread of execution. On the other hand offline training allows us to train on a fixed dataset all at once, but the implementation requires the existence of a big enough dataset to train the Neural Network, more computation effort due to the optimization problem solution in each sample time, and in this approximation, two or more execution threads to meet the sampling time proposed. Finally, both implementations are compare in order to clearly identify the advantages and disadvantages of each. Throughout this thesis it is presented two methodologies for data-driven modeling and control of compliant robot systems. These approaches could enhance the capability of next-generation compliant and flexible robots designed for safe human interaction and uncertain environments. Overall, the results validate the feasibility and advantages of data-driven methods for controlling compliant robots.
- Design of a soft gripper with compliant mechanisms(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-12-06) Puente Flores, Alfredo; Román Flores, Armando; puemcuervo; Cuan Urquizo, Enrique; Vázquez Hurtado, Carlos; School of Engineering and Sciences; Campus Monterrey; Urbina Coronado, Pedro DanielRobotic manipulators can perform repetitive tasks at rates and accuracies that cannot be rivalled by those of human operators. Nowadays, they are rather ubiqitiuos and widely used in different fields. However, that is not all. Robotic manipulators have slowly started to incursion in fields other than manufacturing like that of medicine and agriculture. Because of the wide variety of fields that currently employ robotic manipulators, tasks can be more complex than the usual ones. For this reason, traditional mechanical grippers are not always adequate and there is currently a high demand for grippers that can effectively adapt to grasp a wider variety of objects – especially those that aree fragile or deformable – without damaging them. Current grippers are mostly made of mechanical linkages what makes them stiff and non-adaptive, which is a disadvantage when attempting to grasp delicate objects. Soft grippers can be an adequate solution for this problem and have gained attention in recent years. Although some models have been presented in the literature, they have several drawbacks. This work presents the design of a novel soft gripper that can adapt to the shape of the object. Experiments were conducted to validate the proposal.