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Neural Drone Racer Mentored by Classical Controllers

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Advances in Computational Intelligence (MICAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14391))

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Abstract

The autonomous drone race has driven the development of various approaches to agile flight involving perception, planning and control. The latter is most effective when using external information such as high-frequency drone position. On the other hand, deep learning-based methods propose to train an artificial pilot to learn to associate sensory data with flight commands using only visual information. In this paper, we propose an approach for developing a neural drone racer based on the mentoring of three classical flight controllers combined with visual information. We performed a comparison of the three trained models. Our results demonstrated an improvement in navigation behaviour and increased the speed to complete a racetrack by a factor of 7 compared to that reported in the state of the art.

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Notes

  1. 1.

    Available online: https://acado.github.io/,.

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Author information

Authors and Affiliations

  1. Computer Science Department of the Instituto Nacional de Astrofisica, Optica y Electronica, Puebla, Mexico

    L. Oyuki Rojas-Perez, Alejandro Gutierrez-Giles & Jose Martinez-Carranza

Authors
  1. L. Oyuki Rojas-Perez
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  2. Alejandro Gutierrez-Giles
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  3. Jose Martinez-Carranza
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Corresponding author

Correspondence to Jose Martinez-Carranza .

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Editors and Affiliations

  1. Center for Computing Research, Instituto Politécnico Nacional, Ciudad de México, Distrito Federal, Mexico

    Hiram Calvo

  2. Facultad de Ingeniería, Universidad Panamericana, Ciudad de México, Mexico

    Lourdes Martínez-Villaseñor

  3. Facultad de Ingeniería, Universidad Panamericana, Ciudad de México, Mexico

    Hiram Ponce

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Rojas-Perez, L.O., Gutierrez-Giles, A., Martinez-Carranza, J. (2024). Neural Drone Racer Mentored by Classical Controllers. In: Calvo, H., Martínez-Villaseñor, L., Ponce, H. (eds) Advances in Computational Intelligence. MICAI 2023. Lecture Notes in Computer Science(), vol 14391. Springer, Cham. https://doi.org/10.1007/978-3-031-47765-2_7

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  • DOI: https://doi.org/10.1007/978-3-031-47765-2_7

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