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Differential mapping spiking neural network for sensor-based robot control.

Omar Zahra1, Silvia Tolu2, David Navarro-Alarcon1

  • 1The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China.

Bioinspiration & Biomimetics
|March 11, 2021
PubMed
Summary
This summary is machine-generated.

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This study introduces a spiking neural network (SNN) for robotic control, approximating sensorimotor maps. The SNN enables efficient, real-time robot navigation even with noisy data.

Area of Science:

  • Robotics
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Robotic systems require sophisticated control mechanisms to interpret sensor data and execute motor commands accurately.
  • Approximating sensorimotor maps is crucial for enabling robots to perform complex tasks, but traditional methods can be computationally intensive.
  • Spiking neural networks (SNNs) offer a biologically plausible and potentially more efficient alternative for modeling neural processes.

Purpose of the Study:

  • To propose and validate a spiking neural network (SNN) for approximating differential sensorimotor maps in robotic systems.
  • To develop a control architecture that leverages SNNs for efficient and accurate robot navigation.
  • To demonstrate the SNN's capability in handling noisy sensor data for real-time control.

Main Methods:

Keywords:
roboticssensor-based controlspiking neural networksvisual servoing

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  • A spiking neural network (SNN) architecture was designed with sensory input and motor output layers, utilizing Izhikevich neurons and spike timing-dependent plasticity.
  • A motor babbling process was employed to encode sensor feedback (proprioceptive and exteroceptive) into the SNN.
  • Network parameters were tuned using a proposed guideline and particle swarm optimization.
  • The SNN model was applied to a vision-guided robot for a target reaching task.

Main Results:

  • The SNN effectively approximated local Jacobian-like projections, relating sensor changes to motor changes.
  • The proposed control architecture successfully guided the robot to reach targets, minimizing path deviations and execution time.
  • The SNN demonstrated low data and neuron requirements for training due to its architecture and optimized parameters.
  • The system proved capable of real-time control and handling noisy sensor readings.

Conclusions:

  • Spiking neural networks provide an effective and efficient method for approximating sensorimotor maps in robotic systems.
  • The developed SNN-based control methodology offers a biologically plausible approach for real-time robot navigation with reduced computational demands.
  • The approach shows promise for robust robot control in the presence of sensor noise and limited training resources.