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This work has been supported by the project COPILOT ref. Y2020\EMT6368, funded by the Madrid Government under the R&D Synergic Projects Program. This work has also been supported by the project INSERTION ref. ID2021-127648OBC32, which was funded by the Spanish Ministry of Science and Innovation under the program "Projects for Knowledge Generating". In addition, this work has also been supported by the project RATEC ref: PDC2022-133643-C22 funded by the Spanish Ministry of Science and Innovation. The work of the second author is supported by the Spanish Ministry of Science and Innovation under its Program for Technical Assistants PTA2021-020671. The work of the third author is supported under the Pre-Doctoral Program of Research and Technologic Innovation Department of Madrid Government.
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Fernandez-Cortizas, MiguelAutor (correspondencia)Perez-Saura, DavidAutor o CoautorPerez-Segui, RafaelAutor o CoautorRodriguez-Vazquez, JavierAutor o CoautorCely, Juan SAutor o CoautorCampoy, PascualAutor o CoautorLocal Gaussian Modifiers (LGMs): UAV dynamic trajectory generation for onboard computation
Publicado en:2013 International Conference On Unmanned Aircraft Systems (Icuas). 1101-1108 - 2024-01-01 (), DOI: 10.1109/ICUAS60882.2024.10556985
Autores: Fernandez-Cortizas, M; Perez-Saura, D; Perez-Segui, R; Rodriguez-Vazquez, J; Cely, JS; Campoy, P
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Resumen
Agile autonomous drones are becoming increasingly popular in research due to the challenges they represent in fields like control, state estimation, or perception at high speeds. When all algorithms are computed onboard the UAV, computational limitations make the task of agile flight even more difficult. One of the most computationally expensive tasks in agile flight is the generation of optimal trajectories. When these trajectories must be updated online due to changes in the environment or uncertainties, this high computational cost may result in insufficient time to reach the desired waypoints, which could cause a drone crash in cluttered environments. In this paper, we present Local Gaussian Modifiers (LGMs), a fast and lightweight way of modifying computationally heavy trajectories when recalculating them in time is not possible due to computational limitations. Moreover, we propose a strategy for deciding when is convenient to use these modifiers or recalculate the whole trajectory based on an estimation of the computational time of this trajectory generation. A trajectory blending procedure is also proposed to ensure smoothness in UAV control when a new trajectory is computed. Our approach was validated in simulation, being able to pass through a race circuit with moving gates, achieving speeds up to 16.0 m/s. Real flight validation was also performed achieving speeds up to 4.0 m/s in a fully autonomous pipeline using onboard computing.
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Análisis de liderazgo de los autores institucionales
Existe un liderazgo significativo ya que algunos de los autores pertenecientes a la institución aparecen como primer o último firmante, se puede apreciar en el detalle: Primer Autor (FERNANDEZ CORTIZAS, MIGUEL) y Último Autor (CAMPOY CERVERA, PASCUAL).
el autor responsable de establecer las labores de correspondencia ha sido FERNANDEZ CORTIZAS, MIGUEL.