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A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks.

Dailin Marrero1, John Kern1, Claudio Urrea1

  • 1Electrical Engineering Department, Faculty of Engineering, University of Santiago of Chile (USACH), Av. Víctor Jara 3519, Estación Central, Santiago 9170124, Chile.

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|January 23, 2024
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Summary
This summary is machine-generated.

Spiking neural networks (SNNs) enhance robotic arm trajectory tracking. This brain-inspired approach offers superior accuracy and efficiency over traditional controllers.

Keywords:
NEFNengorobotic controlspiking neural networks

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Area of Science:

  • Robotics
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Spiking neural networks (SNNs) mimic brain function using temporal coding for efficient information processing.
  • Conventional neural networks struggle with temporal information critical for robotic control.
  • SNNs offer potential advantages in adaptability and efficiency for robotic applications.

Purpose of the Study:

  • To investigate SNNs for developing advanced robotic controllers.
  • To improve trajectory tracking accuracy in robotic arms using SNNs.
  • To design and simulate a novel SNN-based controller.

Main Methods:

  • Employed the Neural Engineering Framework (NEF) for controller design.
  • Developed a spiking Proportional-Integral-Derivative (PID) controller.
  • Simulated the controller for a 3-Degrees-of-Freedom (3-DoF) robotic arm using Nengo and MATLAB R2022b.

Main Results:

  • The SNN-based controller demonstrated high accuracy and efficiency in trajectory tracking.
  • Achieved minimal deviations, overshoots, and oscillations during trajectory following.
  • Outperformed a fuzzy controller by 5% and a conventional PID controller by 6% (ITAE) and 30% (RMSE).

Conclusions:

  • NEF and SNNs are effective for developing robotic controllers.
  • SNN-based controllers show significant potential for robotic arm trajectory tracking.
  • This research paves the way for SNNs in dynamic environments and advanced robotics.