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Deep Reinforcement Learning for Autonomous Underwater Navigation: A Comparative Study with DWA and Digital Twin

Zamirddine Mari1, Mohamad Motasem Nawaf2, Pierre Drap2

  • 1DGA Techniques Navales, Direction Générale de l'Armement, 83000 Toulon, France.

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Summary

Autonomous underwater navigation using deep reinforcement learning (DRL) with Proximal Policy Optimization (PPO) outperforms traditional methods in cluttered environments. The PPO agent demonstrated superior obstacle avoidance and adaptability, transferring learned behaviors from simulation to a real robot.

Keywords:
3D reconstructionautonomous underwater vehicledynamic window approachobstacle avoidancereinforcement learning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Marine Technology

Background:

  • Autonomous underwater navigation faces challenges like GPS denial, poor visibility, and obstacles.
  • The BlueROV2 platform is utilized for scientific investigation in these demanding conditions.

Purpose of the Study:

  • To develop and evaluate a deep reinforcement learning approach for autonomous underwater navigation.
  • To compare the proposed method against the Dynamic Window Approach (DWA) for obstacle avoidance.

Main Methods:

  • A Proximal Policy Optimization (PPO) algorithm was employed, integrating target navigation, a virtual occupancy grid, and raycasting for situational awareness.
  • The agent's observation space was designed as a high-dimensional (84-dimensional) input.
  • Performance was evaluated in simulation and on a physical BlueROV2 using a 3D digital twin.

Main Results:

  • The PPO-based policy significantly outperformed the DWA planner in cluttered underwater environments.
  • The PPO agent exhibited enhanced local adaptation and a reduction in collisions.
  • Successful transfer of learned navigation behaviors from simulation to the real world was demonstrated.

Conclusions:

  • Deep reinforcement learning, specifically PPO, is a viable and effective method for autonomous underwater navigation.
  • The proposed observation space design provides comprehensive situational awareness for the navigation agent.
  • The study validates the transferability of simulated DRL policies to physical robotic systems.