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Updated: Jun 12, 2026

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)
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Autonomous pathfinding for underactuated AUVs using FDHNN.

Mingzhi Chen1, Hongfei Li1, Liming Feng1

  • 1School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.

Scientific Reports
|June 10, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a Finite Direction Hopfield Neural Network (FDHNN) and refined Dynamic Window Approach (DWA) for autonomous underwater vehicle (AUV) path planning. The method enhances navigation reliability for underactuated AUVs with directional constraints in underwater exploration.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Oceanography

Background:

  • Autonomous Underwater Vehicles (AUVs) are crucial for marine exploration and mapping.
  • Underactuation and sensor limitations (sonar, visual) pose significant challenges for AUV pathfinding.
  • Existing algorithms often fail to adequately address these underactuation and directional constraints.

Purpose of the Study:

  • To develop an innovative trajectory planning method for underactuated AUVs.
  • To overcome limitations in sensor fields of view and directional constraints during underwater navigation.
  • To improve the reliability and efficiency of AUVs in complex aquatic environments.

Main Methods:

  • Introduction of the Finite Direction Hopfield Neural Network (FDHNN) for global path planning.
  • FDHNN utilizes directional neurons and customized weights for underactuated AUVs and directional constraints.
  • Refinement of the Dynamic Window Approach (DWA) for local maneuvering within the velocity domain.

Main Results:

  • The FDHNN-DWA method demonstrates robust performance in simulations.
  • The proposed method outperforms advanced algorithms like dual deep Q-learning and Dubins paths.
  • Significant improvements in navigation reliability for AUVs with start and end directional limitations were observed.

Conclusions:

  • The FDHNN-DWA trajectory planning method offers a substantial advancement for underactuated AUVs.
  • This approach enhances underwater detection and exploration capabilities.
  • The method provides adaptable and reliable navigation solutions for diverse aquatic environments.