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Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
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AUV Path Planning Considering Ocean Current Disturbance Based on Cloud Desktop Technology.

Siyuan Hu1, Shuai Xiao2, Jiachen Yang2

  • 1School of Futrue Technology, Tianjin University, Tianjin 300072, China.

Sensors (Basel, Switzerland)
|September 9, 2023
PubMed
Summary
This summary is machine-generated.

Autonomous Underwater Vehicles (AUVs) navigate dynamic oceans using a novel N-DDQNP model for efficient path planning. This AI approach improves exploration and reduces navigation time in complex marine environments.

Keywords:
cloud desktopdeep learningocean currentocean datapath planning

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Area of Science:

  • Marine robotics and ocean exploration.
  • Artificial intelligence in autonomous systems.
  • Navigation and path planning algorithms.

Background:

  • Autonomous Underwater Vehicles (AUVs) are vital for ocean exploration but face navigational challenges due to unpredictable ocean currents.
  • Effective path planning in dynamic marine environments is critical for AUV task performance and safety.
  • Existing deep Q-network (DQN) algorithms suffer from slow convergence and limited exploration.

Purpose of the Study:

  • To develop an improved path planning model for AUVs in dynamic ocean environments.
  • To address the limitations of traditional DQN algorithms in exploration and convergence speed.
  • To enhance AUV navigation safety and efficiency for ocean energy detection and marine resource exploration.

Main Methods:

  • Proposed the noise net double DQN network with prioritized experience replay (N-DDQNP) model.
  • Integrated a noise network for enhanced exploration and prioritized experience replay for faster convergence.
  • Developed a compound reward function considering ocean currents, distance to target, and obstacle avoidance.

Main Results:

  • The N-DDQNP model demonstrated superior path planning time compared to other algorithms in varied ocean current and obstacle scenarios.
  • Experiments using real ocean data validated the model's effectiveness in complex environments.
  • A user console-AUV connection via cloud desktop technology was established for intuitive monitoring.

Conclusions:

  • The N-DDQNP model offers a significant advancement in AUV path planning for dynamic ocean environments.
  • The compound reward function effectively guides AUVs by integrating environmental factors.
  • Cloud desktop integration enhances AUV operational safety and efficiency in underwater exploration tasks.