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.
Browse
Search Results
- Neural network circuit implementation using operational amplifiers and digital potentiometers(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-06-09) Posada Hoyos, Jacobo; GOMEZ ESPINOSA, ALFONSO; 57957; Gómez Espinosa, Alfonso; puemcuervo; Escobedo Cabello, Jesus Arturo; Domínguez Oviedo, Agustín; González García, Josué; School of Engineering and Sciences; Campus Monterrey; Valdés Aguirre, BenjamínImplementations of Artificial Neural Networks (ANN) have been advancing for almost three decades and their importance has been marked by the different methods used in their construction, their applications, and comparisons in terms of speed, costs, and performance between implementations made by software and hardware. As analog implementations of ANN have been shown to have good levels of performance, high processing speed, low power consumption, small size, and low cost, they have played an important role in the development of new designs. This work presents a proposal to design a circuit implementation of an ANN by using Operational Amplifiers (Opamps) and digital potentiometers to create a network that can be trained by using an external training system. This, based on circuit analysis and training algorithm by the back propagation (BP) approach. The proposed design will be simulated in the circuit simulator Proteus. The circuit is tested using the logical gates benchmark problem to verify its performance with the BP learning algorithm. The results of this work demonstrate that it is possible to create a neural network using analogous components. Furthermore, it shows good performance when implementing the training algorithm using digital potentiometers. As future work is expected to improve the performance of training to create a controller based on neural networks and thus, perform the control of a dynamic system.
- Trajectory planning for mobile robot in dynamic environment using LSTM neural network(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-06-03) Molina Leal, Alejandra; Gómez Espinosa, Alfonso; tolmquevedo; Cuan Urquizo, Enrique; Cruz Ramírez, Sergio Rolando; Escuela de Ingeniería y Ciencias; Campus Monterrey; Escobedo Cabello, Jesús ArturoAutonomous mobile robots are an important focus of current research due to the advantages they bring to the industry, such as performing dangerous tasks with greater precision than humans. An autonomous mobile robot must be able to generate a collision-free trajectory while avoiding static and dynamic obstacles from the specified start location to the target location. Machine Learning, a sub-field of Artificial Intelligence, is applied to create a Long Short-Term Memory (LSTM) neural network that is implemented and executed to let a mobile robot find the trajectory between two points and navigate while avoiding a dynamic obstacle. The input of the network is the distance between the mobile robot and the obstacles thrown by the LiDAR sensor, the desired target location, and the mobile robot location with respect to the odometry reference frame. Using the model to learn the mapping between input and output in the sample data, the linear and angular velocity of the mobile robot are obtained. The mobile robot and its dynamic environment are simulated in Gazebo, which is an open-source 3D robotics simulator. Gazebo can be synchronized with ROS (Robot Operating System). The computational experiments show that the network model can plan a safe navigation path in a dynamic environment.
- Synthesis of a finite-time convergence controller for trajectory tracking of unmanned underwater vehicles(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-06) Narcizo Nuci, Néstor Alejandro; Salgado Jiménez, Tomás; 201232; Gómez Espinosa, Alfonso; emipsanchez; Salgado Jiménez, Tomás; González García, Josué; Escuela de Ingeniería y Ciencias; Campus Monterrey; García Valdovinos, Luis GovindaUnmanned underwater vehicles have gained importance since they can perform tasks in underwater environments such as exploration and construction. Proper control of the vehicle trajectory is fundamental for successfully complete a task. When disturbances are frequent and the dynamics of the vehicle change, fast response of the control scheme is required and the classical controllers do not adapt to overcome these conditions. As the main contribution of this work, we propose the synthesis and implementation of a control scheme with finite-time convergence applied to the trajectory tracking including a time variable gain in the sliding surface of a 2nd Order Sliding Mode Control. In the first part, the parameterized trajectory considered five degrees of freedom: x,y,z, \phi, and \psi. In a second part, an emulation of a simultaneous scheme between two vehicles is proposed, taking advantage of the finite-time convergence of the proposed controller. The dynamic parameterization of the vehicle is based on the BlueROV2 vehicle by BlueRobotics, which counts with four horizontal and vectored thrusters, and two vertical thrusters. A finite-time second-order sliding-mode controller will be synthesized by applying a variable gain on the sliding surface. This gain will be parametrized by a Time Base Generator. The controller was tested to determine its performance, accuracy, and prompt response for trajectory tracking in space and was compared against classical controllers: a Proportional-Integral-Derivative Controller, a Feedback Linearization controller, and a Lyapunov function-based controller. In the second part, the controller was compared with two state-of-the-art controllers, that also count with finite-time convergence. The proposed control schemes will be evaluated in a simulator constructed in a Matlab/ Simulink environment with the actual parameters of the underwater vehicle, and where the parameters of the RMS values of the tracking error and the RMS values of the control signals are analyzed to evaluate the performance of the controllers. The results of this work demonstrated that it is possible to synthesize the 2nd Order Sliding Mode Controller with finite-time convergence and apply it in the trajectory tracking of underwater vehicles, in trajectories that involved the five degrees of freedom, and even in the presence of marine currents. The results of this thesis are expected to be implemented in future work related to trajectory tracking and collaborative tasks with underwater vehicles.
- Robotic-computer vision system for 3D welding trajectories(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-06) Rodríguez Suárez, Jesús Braian; GOMEZ ESPINOSA, ALFONSO; 57957; Gómez Espinosa, Alfonso; emipsanchez; Escobedo Cabello, Jesús Arturo; Swenson Durie, Rick Leigh; Escuela de Ingeniería y Ciencias; Campus Monterrey; Cuan Urquizo, EnriqueThe necessity for intelligent welding robots that meet the demand in the real industrial production, according to the objectives of Industry 4.0, has been supported thanks to the rapid development of computer vision and the use of new technologies. In order to improve the efficiency in weld location for industrial robots, this work focuses on trajectory extraction based on color features identification over three-dimensional surfaces acquired with a depth-RGB sensor. The system is planned to be used with a low-cost Intel RealSense D435 sensor for the reconstruction of 3D models based on stereo vision and the built-in color sensor to quickly identify the objective trajectory, since the parts to be welded are previously marked with different colors, indicating the locations of the welding trajectories to be followed. This work focuses on the use of point cloud and a color data to obtain a three-dimensional model of the workpiece with which the points of the target trajectory are segmented by color thresholds in the RGB and the HSV color space, finally a spline cubic interpolation algorithm is implemented to obtain a smooth trajectory. Experimental results show that the RMSE error for V-type butt-joint path extraction is under 1.1 mm and below 0.6 mm for a straight butt joint, showing a suitable system for welding bead of various shapes and materials. It is important to note that to demonstrate its application in a robotic environment, the expected results will be presented in virtual environments created on the Robot Operating System (ROS) software.
- Sensor data fusion for a mobile robot using a neural network algorithm(Instituto Tecnológico y de Estudios Superiores de Monterrey) Barreto Cubero, Andrés Javier; GOMEZ ESPINOSA, ALFONSO; 57957; Gómez Espinosa, Alfonso; puelquio, emipsanchez; Cuan Urquizo, Enrique; Cruz Ramírez, Sergio Rolando; Escuela de Ingeniería y Ciencias; Campus Monterrey; Escobedo Cabello, Jesús ArturoMobile robots must be capable to obtain an accurate map of their surroundings and move within it. To detect different materials that might be undetectable to one sensor but not others it is necessary to have at least two sensors, with this is possible to generate a 2D occupancy map that is as close to reality as possible. In this thesis, an artificial neural network is used to fuse data from a tri-sensor (Intel RealSense Stereo Camera, 2D 360° LiDAR-Light Detection and Ranging Sensor and an HC-SR04 Ultrasonic Sensor) setup capable of detecting glass, polished metals, brick walls, wooden panels and other materials typically found in indoor environments. When a map is to be compiled out of different sensor’s data, it is necessary to implement a preprocessing scheme to filter all the outliers in the data for each sensor. Then, run a data fusion algorithm to integrate all the information into a single, more accurate 2D map that considers all sensor’s information. The Robotis Turtlebot 3 Waffle Pi robot is used as an experimental platform along with Robotic Operating System as the main Human Machine Interface to implement the algorithms. Test results show that with the fusion algorithm implemented, it is possible to detect glass and other obstacles invisible to the LiDAR with an estimated root-mean-square error of 4 cm with multiple sensor configurations.