Modular Control Architecture for Safe Marine Navigation: Reinforcement Learning and Predictive Safety Filters

Many autonomous systems face safety challenges, requiring robust closed-loop control to handle physical limitations and safety constraints. Real-world systems, like autonomous ships, encounter nonlinear dynamics and environmental disturbances. Reinforcement learning is increasingly used to adapt to...

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Bibliographic Details
Published in:arXiv.org 2024-04
Main Authors: Vaaler, Aksel, Husa, Svein Jostein, Menges, Daniel, Thomas Nakken Larsen, Rasheed, Adil
Format: Article
Language:English
Subjects:
Closed loops
Dynamical systems
Feedback control
Naval engineering
Nonlinear dynamics
Robust control
Safety
Surface navigation
Trajectory planning
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Summary:Many autonomous systems face safety challenges, requiring robust closed-loop control to handle physical limitations and safety constraints. Real-world systems, like autonomous ships, encounter nonlinear dynamics and environmental disturbances. Reinforcement learning is increasingly used to adapt to complex scenarios, but standard frameworks ensuring safety and stability are lacking. Predictive Safety Filters (PSF) offer a promising solution, ensuring constraint satisfaction in learning-based control without explicit constraint handling. This modular approach allows using arbitrary control policies, with the safety filter optimizing proposed actions to meet physical and safety constraints. We apply this approach to marine navigation, combining RL with PSF on a simulated Cybership II model. The RL agent is trained on path following and collision avpodance, while the PSF monitors and modifies control actions for safety. Results demonstrate the PSF's effectiveness in maintaining safety without hindering the RL agent's learning rate and performance, evaluated against a standard RL agent without PSF.
ISSN:2331-8422