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Visual Servoing for an Autonomous Hexarotor Using a Neural Network Based PID Controller.

Carlos Lopez-Franco1,2, Javier Gomez-Avila3, Alma Y Alanis4

  • 1Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. Marcelino García Barragán 1421, Guadalajara C.P. 44430, Jalisco, Mexico. carlos.lopez@cucei.udg.mx.

Sensors (Basel, Switzerland)
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Summary
This summary is machine-generated.

This study presents a novel neural proportional integral derivative (PID) servo control for unmanned aerial vehicles (UAVs), enabling precise hexarotor positioning using vision sensors and a velocity vector reference.

Keywords:
hexarotorunmanned aerial vehiclevisual servoing

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Area of Science:

  • Robotics
  • Control Systems
  • Computer Vision

Background:

  • Unmanned aerial vehicles (UAVs) face challenges with high nonlinearities and lack of onboard global positioning sensors.
  • Accurate real-time position estimation is crucial for advanced UAV control applications.

Purpose of the Study:

  • To develop a real-time image-based visual servo control (IBVS) for hexarotors.
  • To address nonlinearities and unknown positions in UAVs using integrated vision sensors and neural PID control.

Main Methods:

  • Implemented a servo control system integrating vision sensors with a neural proportional integral derivative (PID) controller.
  • Utilized a velocity vector as a reference for hexarotor position control.
  • Ensured tight coordination between control algorithms, system models, sensors, and hardware/software platforms for real-time execution.

Main Results:

  • Demonstrated the effectiveness of sensor integration and the neural PID control algorithm on a highly nonlinear system.
  • Validated the approach through experiments and simulations on the AscTec Firefly platform.
  • Successfully achieved real-time control of hexarotor position using visual servoing.

Conclusions:

  • The proposed neural PID-based IBVS system effectively overcomes the limitations of nonlinearities and unknown positions in UAVs.
  • Real-time implementation of complex control strategies is feasible with careful integration of components.
  • This approach offers a robust solution for precise UAV navigation and control.