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
- Monocular obstacle avoidance framework for autonomous navigation(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06) Abascal Molina, Andrea; Sotelo Molina, David Alejandro; emipsanchez; Muñoz Ubando, Luis Alberto; Pinto Orozco, Arturo; School of Engineering and Sciences; Campus Monterrey; Sotelo Molina, Carlos GustavoThis thesis presents a vision-based autonomous navigation framework that integrates deep learning-based monocular depth estimation with a Model Predictive Control (MPC) strategy for dynamic obstacle avoidance in indoor Unmanned Aerial Vehicles (UAVs). The proposed system addresses key challenges of operating in cluttered indoor environments where tradi- tional localization and depth sensing solutions are impractical due to hardware constraints or environmental limitations. Leveraging a fine-tuned Depth Anything V2 model, the frame- work generates dense depth maps in real time and utilizes them to construct sector-based spatial constraints within the UAV’s visual field. These constraints are incorporated into the MPC formulation to inform predictive control decisions and enable safe trajectory planning. A visual feature extraction module based on marker detection provides the reference trajec- tory for visual servoing, while the UAV continuously updates its path to avoid obstacles using dynamic depth constraints. The system was experimentally validated on a Tello quadrotor in various indoor scenarios, including static target alignment, dynamic target tracking, and ob- stacle intrusion. The results demonstrate reliable visual tracking, real-time depth estimation reaching 40 Hz via TensorRT optimization, and successful avoidance behavior under com- plex visual conditions. The contributions of this work include the design of a lightweight real-time perception-to-control pipeline, the integration of DL-based depth constraints into an MPC framework, and the demonstration of safe, closed-loop UAV navigation in dynamic environments. Although the system is designed for aerial robots, its modular architecture and sensor-driven control strategy generalize to other mobile robotic platforms. Ultimately, this framework equips mobile robots with advanced perception capabilities that are essential for achieving higher levels of autonomy in complex and unstructured environments.
- Implementation of a Long Short-Term Memory neural network-based algorithm for dynamic obstacle avoidance(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06-10) Mulás Tejeda, Esmeralda; Gómez Espinosa, Alfonso; emimmayorquin; Cantoral Ceballos, José Antonio; School of Engineering and Sciences; Campus Querétaro; Escobedo Cabello, Jesús ArturoAutonomous mobile robots are essential to the industry, and human-robot interactions are becoming more common nowadays. These interactions require that the robots navigate scenarios with static and dynamic obstacles in a safely manner, avoiding collisions. This paper presents a physical implementation of a method for dynamic obstacle avoidance using a Long Short-Term Memory (LSTM) neural network that obtains information from the mobile robot’s LiDAR for it to be capable of navigating through scenarios with static and dynamic obstacles while avoiding collisions and reaching its goal. The model is implemented using a TurtleBot3 mobile robot within an OptiTrack Motion Capture (MoCap) system for obtaining its position at any given time. The user operates the robot through these scenarios, recording its LiDAR readings, target point, position inside the MoCap system, and its linear and angular velocities, all of which serve as the input for the LSTM network. The model is trained on data from multiple user-operated trajectories across five different scenarios, outputting the linear and angular velocities for the mobile robot. Physical experiments prove that the model is successful in allowing the mobile robot to reach the target point in each scenario while avoiding the dynamic obstacle, with a validation accuracy of 97.99%