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DRL system enables end-to-end motion planning for underwater vehicles

By PulseAugur Editorial · [2 sources] ·

Researchers have developed an end-to-end deep reinforcement learning system for autonomous underwater vehicles (AUVs) that maps raw sensor data directly to thruster commands. This hierarchical approach splits the task into high-level goal generation and low-level command execution, trained using methods like RLPD and SAC with HER. Evaluated in simulation, the system demonstrated effective obstacle avoidance and robustness to sensor noise, though it showed limitations in generalizing to novel obstacle shapes. AI

IMPACT Demonstrates a promising path for simplifying AUV control systems and improving navigation capabilities in complex underwater environments.

RANK_REASON This is a research paper detailing a novel approach to motion planning for AUVs using DRL.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Elisei Shafer, Oren Gal ·

    Towards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning

    arXiv:2606.08513v1 Announce Type: cross Abstract: Autonomous Underwater Vehicles (AUVs) traditionally rely on complex, heavily engineered pipelines for perception, path planning, and motion control. This paper explores the feasibility of an end-to-end Deep Reinforcement Learning …

  2. arXiv cs.LG TIER_1 English(EN) · Oren Gal ·

    Towards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning

    Autonomous Underwater Vehicles (AUVs) traditionally rely on complex, heavily engineered pipelines for perception, path planning, and motion control. This paper explores the feasibility of an end-to-end Deep Reinforcement Learning (DRL) approach that maps raw sensor data directly …

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