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Image Processing for UAV Autonomous Navigation Applying Self-configuring Neural Network

In: Integral Methods in Science and Engineering

Author

Listed:
  • Gerson da Penha Neto

    (Instituto Nacional de Pesquisas Espaciais (INPE))

  • Haroldo F. de Campos Velho

    (Instituto Nacional de Pesquisas Espaciais (INPE))

  • Elcio H. Shiguemori

    (Departamento de Ciência e Tecnologia Aeroespacial (DCTA))

  • José Renato G. Braga

    (Instituto Nacional de Pesquisas Espaciais (INPE))

Abstract

Application and development of Unmanned Aerial Vehicles (UAV) have had a rapid growth. The flight control of these aircrafts can be performed remotely or autonomously. There are different strategies for the UAV autonomous navigation. The positioning estimation can be done by using inertial sensors and General Navigation Satellite Systems (GNSS). The use of the GNSS signal can present some difficulties: natural or not natural interference. An alternative for positioning adjustment is to use a data fusion from different sensors by a Kalman filter. A supervised artificial network (ANN) is trained to emulate the filter for reducing the computational effort. An automatic best topology for the neural network is obtained by minimizing a functional by a new meta-heuristic called Multi-Particle Collision Algorithm (MPCA). Our results show similar accuracy between the ANN and the Kalman filter, with better processing performance to the neural network.

Suggested Citation

  • Gerson da Penha Neto & Haroldo F. de Campos Velho & Elcio H. Shiguemori & José Renato G. Braga, 2019. "Image Processing for UAV Autonomous Navigation Applying Self-configuring Neural Network," Springer Books, in: Christian Constanda & Paul Harris (ed.), Integral Methods in Science and Engineering, chapter 0, pages 321-332, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-16077-7_25
    DOI: 10.1007/978-3-030-16077-7_25
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