Modular Control Architecture for Safe Marine Navigation: Reinforcement Learning and Predictive Safety Filters
This work addresses safety constraints for autonomous ships, but it is incremental as it applies existing predictive safety filters to a specific domain.
The paper tackled the safety challenges in autonomous marine navigation by combining reinforcement learning with predictive safety filters, demonstrating that the safety filter maintains safety without hindering learning rate and performance compared to a standard RL agent.
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.