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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- Design and implementation of a cascaded MPC with a dynamic battery-aware auxiliary reference for unmanned aerial vehicles(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06-12) Borbolla Burillo, Patricio; Sotelo Molina, David Alejandro; emimmayorquin; Frye, Michael; Beltran Carbajal, Francisco; School of Engineering and Sciences; Campus Monterrey; Sotelo Molina, Carlos GustavoUnmanned Aerial Vehicles (UAVs) have been a focus of interest within researchers and robotics industry due to their versatility and potential to improve a wide range of applications. Nevertheless, modeling control of UAVs, especially quadrotors, remains difficult due to the significant link between position and orientation dynamics. Although conventional control strategies are widely used for their simplicity and ease of implementation, they have a poor performance in practical applications, especially under uncertain operating conditions. For this reason, numerous advanced control strategies have been implemented to improve trajectory tracking. However, drone efficiency in practical tasks is also affected by flight autonomy, and these algorithms do not consider energy consumption rates. Moreover, even though battery management systems (BMS) have been designed to prevent overdischarges, they do not consider voltage sags caused by thrust variations. In this thesis, these issues are addressed through the implementation of a proposed control structure that consists of two main components: a model predictive control (MPC) strategy and a dynamic battery-aware auxiliary reference (DBAR) system. First, the design and implementation of a hierarchical cascaded MPC for three-dimensional trajectory tracking is presented. In contrast to previous research, the heavy computational burden is managed by decomposing the overall MPC strategy into two different schemes. The first scheme controls the translational displacements, while the second scheme regulates the rotational movements of the quadrotor. On the other hand, the DBAR system defines a transient reference in the x, y, and z axes according to the positional error of the drone and a desired voltage sag. For validation, the proposed controller’s performance is compared with a proportional–integral–velocity (PIV) controller from the literature in five real-time scenarios. The experimental results demonstrate that the proposed controller outperforms the PIV controller in terms of trajectory tracking, regulatory control, and flight time extension. Furthermore, the proposed DBAR system allows the MPC to substantially extend the flight duration without significantly affecting regulatory control and trajectory tracking performance, henceforth, unexpected premature system shutdowns are prevented.
- 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.