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Intelligent Smart Marine Autonomous Surface Ship Decision System Based on Improved PPO Algorithm.

Wei Guan1, Zhewen Cui1, Xianku Zhang1

  • 1Navigation College, Dalian Maritime University, Dalian 116026, China.

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Summary
This summary is machine-generated.

This study introduces an improved AI approach for smart marine autonomous surface ships (SMASS) to navigate complex waterways. The method enables unmanned vessels to autonomously plan paths and make decisions, mimicking human-like manipulation.

Keywords:
NomotoPPOSMASSdecision-makingdeep reinforcement learning

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Area of Science:

  • Marine robotics
  • Artificial intelligence
  • Autonomous navigation systems

Background:

  • The increasing complexity of maritime operations necessitates advanced autonomous capabilities for surface vessels.
  • Intelligent smart marine autonomous surface ships (SMASS) require sophisticated behavior decision-making and path planning for safe and efficient navigation.
  • Existing methods often rely on human expertise or predefined rules, limiting adaptability in dynamic environments.

Purpose of the Study:

  • To develop and evaluate an improved Proximal Policy Optimization (PPO) based approach for local path planning and behavior decision-making in unmanned SMASS.
  • To enable SMASS to navigate to a target destination without prior human experience or explicit programming of all scenarios.
  • To enhance the self-learning and continuous optimization capabilities of autonomous marine systems.

Main Methods:

  • Modeling the SMASS using the Nomoto model within a simulated waterway environment.
  • Implementing an improved Proximal Policy Optimization (PPO) algorithm incorporating generalized advantage estimation for self-adjusting baselines.
  • Utilizing a reward/punishment system based on distance, obstacles, and prohibited areas to guide the learning process.
  • Training a neural network model to learn the action-reward relationship for manipulating the SMASS's movement.

Main Results:

  • The trained neural network successfully guided the SMASS to navigate towards its target, demonstrating effective path planning and decision-making.
  • The improved PPO approach allowed the SMASS to autonomously discover optimal navigation strategies and paths to maximize rewards.
  • The system exhibited enhanced self-learning and continuous optimization capabilities compared to existing methods.
  • The learned manipulation strategies closely resembled human-like control and decision-making.

Conclusions:

  • The proposed improved PPO approach offers a robust and effective solution for autonomous path planning and behavior decision-making in SMASS.
  • This AI-driven method significantly enhances the adaptability and operational efficiency of unmanned marine vessels.
  • The research contributes to the advancement of intelligent maritime systems, paving the way for more sophisticated autonomous operations at sea.