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A Novel Obstacle Traversal Method for Multiple Robotic Fish Based on Cross-Modal Variational Autoencoders and

Ruilong Wang1, Ming Wang1, Qianchuan Zhao2

  • 1School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China.

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|April 26, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multi-robotic fish navigation system using cross-modal variational autoencoder (CM-VAE) and imitation learning for obstacle traversal. The method enables stable navigation in complex underwater environments, outperforming existing techniques.

Keywords:
CM-VAEimitation learningmultiple robotic fishvisual navigation

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Precision control of multiple robotic fish in complex underwater environments presents significant challenges.
  • Existing methods struggle with stable visual navigation and obstacle traversal for multi-robotic systems.

Purpose of the Study:

  • To propose and validate a novel multi-robotic fish obstacle traversal technique.
  • To enhance the visual navigation capabilities of robotic fish in complex underwater settings.

Main Methods:

  • Utilized a cross-modal variational autoencoder (CM-VAE) to encode first-person robotic fish views into a low-dimensional latent space.
  • Employed imitation learning to map latent features to robotic fish velocity commands.
  • Conducted experiments with single and multiple robotic fish on linear, S-shaped, and circular gate frame trajectories.

Main Results:

  • The proposed visual navigation method demonstrated stable traversal of various gate frame trajectories.
  • The CM-VAE and imitation learning approach showed superior performance compared to end-to-end learning and unsupervised image reconstruction.
  • Successfully enabled intelligent navigation for robotic fish in complex environments.

Conclusions:

  • The developed technique offers a robust solution for multi-robotic fish visual navigation.
  • This approach provides a new pathway for intelligent underwater robotic systems.
  • The method significantly improves obstacle traversal and navigation stability in challenging underwater conditions.