Browsing by Author "Flores Campos, Juan Alejandro"
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Item Open Access An advanced path planning and UAV relay system: enhancing connectivity in rural environments(MDPI, 2024-03-06) El Debeiki, Mostafa; Al-Rubaye, Saba; Perrusquía, Adolfo; Conrad, Christopher; Flores Campos, Juan AlejandroThe use of unmanned aerial vehicles (UAVs) is increasing in transportation applications due to their high versatility and maneuverability in complex environments. Search and rescue is one of the most challenging applications of UAVs due to the non-homogeneous nature of the environmental and communication landscapes. In particular, mountainous areas pose difficulties due to the loss of connectivity caused by large valleys and the volumes of hazardous weather. In this paper, the connectivity issue in mountainous areas is addressed using a path planning algorithm for UAV relay. The approach is based on two main phases: (1) the detection of areas of interest where the connectivity signal is poor, and (2) an energy-aware and resilient path planning algorithm that maximizes the coverage links. The approach uses a viewshed analysis to identify areas of visibility between the areas of interest and the cell-towers. This allows the construction of a blockage map that prevents the UAV from passing through areas with no coverage, whilst maximizing the coverage area under energy constraints and hazardous weather. The proposed approach is validated under open-access datasets of mountainous zones, and the obtained results confirm the benefits of the proposed approach for communication networks in remote and challenging environments.Item Open Access Robust control of linear systems: a min-max reinforcement learning formulation(IEEE, 2023-12-05) Flores Campos, Juan Alejandro; Perrusquía, AdolfoIn this paper, an online robust controller based on a min-max reinforcement learning approach for linear systems is discussed. Disturbances are represented by external signals coupled with the control input which are assumed to be bounded within a set of admissible disturbances. The proposed controller implements a min-max approach which realizes a smooth transition between optimal and robust controllers. Lyapunov stability theory is used to assess the stability and boundedness of the min-max robust formulation. A neural reinforcement learning architecture is used to obtain an approximation of the parameters associated to the optimal cost. Simulations are carried out to validate the proposed approach.