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Designing controllers can be highly dependent on the knowledge of the underlying model, especially in aquatic environments where the hydrodynamics parameters of the vehicles are almost always unknown. This work leverages this difficulty by using a machine learning-based guidance subsystem that produces valid control outputs, not only for one unmanned surface vessel but also for a group of them so that they can efficiently move while maintaining a formation. The formation control network was trained using proximal policy optimization (PPO) and through a simulator considering the well-known Cybership-II ship. Results show that the actor-critic deep reinforcement learning network produces an efficient policy to control a fleet of unmanned surface vehicles (USVs) and maintains formation whilst moving towards a goal.

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Unmanned Underactuated Surface Vehicle Formation Control Using Deep Reinforcement Learning

  • 章节

  • 发表日期：

    2026年

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  • Luciano Villarreal,
  • Federico Peralta,
  • Pablo Millán,
  • Guillermo Bejarano

摘要

Designing controllers can be highly dependent on the knowledge of the underlying model, especially in aquatic environments where the hydrodynamics parameters of the vehicles are almost always unknown. This work leverages this difficulty by using a machine learning-based guidance subsystem that produces valid control outputs, not only for one unmanned surface vessel but also for a group of them so that they can efficiently move while maintaining a formation. The formation control network was trained using proximal policy optimization (PPO) and through a simulator considering the well-known Cybership-II ship. Results show that the actor-critic deep reinforcement learning network produces an efficient policy to control a fleet of unmanned surface vehicles (USVs) and maintains formation whilst moving towards a goal.