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
- Priority-aware collision avoidance via optimal velocity in multi-robot systems(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025) Sánchez Vaca, Luis Humberto; Sánchez Ante, Gildardo; mtyahinojosa, emipsanchez; Castañeda Cuevas, Herman; Hinojosa Cervantes, Salvador Miguel; Mercado Ravell, Diego Alberto; Escuela de Ingeniería y Ciencias; Campus Monterrey; Abaunza González, HernánThis thesis presents a decentralized control framework for prioritized multi-robot navigation that integrates Reciprocal Velocity Obstacles (RVO) with Bare-Bones Particle Swarm Optimization (BB-PSO). While velocity-based methods provide real-time geometric collision-avoidance guarantees, they often lead to oscillatory or conservative behaviors in dense environments and do not account for heterogeneous task priorities. On the other hand, optimization-based planners can shape agent behavior but lack inherent safety guarantees unless they are explicitly constrained. To address these limitations, the proposed framework combines two paradigms. First, RVO constructs a set of safe and admissible velocities. Then, BB-PSO selects the optimal velocity within this set based on a cost function that integrates priority-aware behaviors. This mechanism enables robots to navigate smoothly while respecting different task urgencies. Each robot independently computes its control command using local information about other agents, making this a fully decentralized operation. A simulation framework was developed to evaluate the proposed method across scenarios with different robot densities, priority distributions, and motion constraints. Experiments compare the hybrid controller against a greedy baseline using three metrics: arrival time, distance traveled, and collision occurrences. Results show that the hybrid approach improves navigation efficiency and significantly benefits high-priority agents by reducing their travel time and path deviation while maintaining safe interactions for the entire team. Overall, this thesis contributes a novel prioritized navigation strategy that combines geometric safety, real-time feasibility, and adaptive optimization. The approach represents a promising step toward scalable, priority-aware multi-robot systems that operate in complex and dynamic environments, with potential applications in automated warehouses, hospital logistics, and service robotics.
- A hybrid metaheuristic optimization approach for the synthesis of operating procedures for optimal drum-boiler startups(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020) Garduño Hernández, Emilio; BATRES PRIETO, RAFAEL; 589386; NOGUEZ MONROY, JUANA JULIETA; 202512; PONCE CRUZ, PEDRO; 31857; Batres Prieto, Rafael; RR; Noguez Monroy, Juana Julieta; Ponce Cruz, Pedro; Escuela de Ingeniería y Ciencias; Campus Ciudad de MéxicoA steam generator serves as a power generation equipment that uses the expansive power of the steam to generate electricity. The startup process of a steam generator plays an important role in the ability of a power plant to adjust its electricity generation to changes in demand. As renewable generation plants increase, the levels of variability in electricity production increase. Fast startups become instrumental as they enable traditional power generation plants to provide the quantity of electricity missing when variable renewable energies cannot satisfy demand. A main equipment involved in the startup process of the steam generator is the drum boiler. However, if the startup process is carried out too fast, excessive thermal stresses can occur and provoke damage to the components of the drum boiler. This thesis proposes a dynamic optimization methodology to synthesize operating valve profiles that minimize the startup time of the drum boiler while avoiding the excessive formation of thermal stresses. Since valve operations influence the time-varying behavior of the steam, dynamic simulation is needed in order to evaluate the operating procedure. This thesis proposes a dynamic optimization approach with a hybrid-metaheuristic algorithm that generates the optimal startup procedure of a drum boiler. The proposed algorithm is based on two important elements of two metaheuristic algorithms. Namely, the search zone in the cooling element from the simulated annealing algorithm and the efficient computational performance provided from the tabu search algorithm memory structures. A case study evaluates the proposed approach by comparing it against results previously published in the literature.