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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- 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.