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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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Ring attractor bio-inspired neural network for social robot navigation.

Jesús D Rivero-Ortega1, Juan S Mosquera-Maturana1, Josh Pardo-Cabrera1

  • 1Department of Engineering, Universidad Autónoma de Occidente, Cali, Colombia.

Frontiers in Neurorobotics
|September 18, 2023
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Summary
This summary is machine-generated.

This study presents a bio-inspired robot navigation system using ring attractor neural networks to guide social agents safely. The novel approach enhances human-robot interaction by outperforming existing methods in obstacle avoidance and social comfort.

Keywords:
bio-inspired navigationdecision-makingmotor controlobstacle avoidancering attractor networksrobot guidancesocial navigation

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Area of Science:

  • Robotics
  • Neuroscience
  • Artificial Intelligence

Background:

  • Robot navigation in crowded environments presents challenges in safety and social interaction.
  • Existing methods like the Social Force Model have limitations in handling dynamic obstacles and social cues.

Purpose of the Study:

  • To introduce a bio-inspired navigation system for robots guiding social agents.
  • To enhance safety and comfort in human-robot interactions within complex environments.

Main Methods:

  • A bio-inspired navigation system utilizing ring attractor neural networks for perception, planning, and control.
  • Simulated experiments in a virtual pedestrian area with static and dynamic obstacles.
  • Comparison with Social Force Model and Rapidly Exploring Random Tree Star using Social Individual Index and Relative Motion Index.

Main Results:

  • The proposed navigation system effectively guides social agents while avoiding obstacles.
  • The system demonstrated superior performance compared to the Social Force Model based on evaluation metrics.
  • The integration of specific indices improved collision avoidance and social comfort.

Conclusions:

  • The bio-inspired navigation system offers a promising approach for safer and more comfortable human-robot interactions.
  • Ring attractor neural networks provide a robust framework for integrated navigation control.
  • The developed metrics are effective in evaluating social navigation systems.