PIER: Physics-Informed, Energy-efficient, Risk-aware Routing

Description:

PIER is a cutting-edge maritime routing technology leveraging offline reinforcement learning combined with physics-based modeling and multi-objective optimization. It features a three-stage architecture that accurately predicts vessel speed, generates Pareto-optimal routes balancing time, fuel, and risk, and executes safe, fuel-efficient navigation with a formal safety shield. Tested in real scenarios like the Gulf of Mexico, PIER achieves significant CO₂ reductions and rapid computation, offering a scalable, simulator-free solution for the shipping industry.

Key Advantages:

• Real-time route optimization with sub-millisecond inference latency.
• Eliminates need for costly or proprietary simulators through offline reinforcement learning.
• Balances travel time, fuel consumption, and wave exposure risk via multi-objective planning.
• Three-layer formal safety shield ensures collision-free navigation.
• Large-scale data-driven physics-constrained speed prediction model.
• Significant CO₂ emissions reduction potential (14–36 Mt CO₂/year globally).
• Commercially viable for integration with voyage optimization and maritime insurance platforms.

Problems Solved:

• High fuel consumption and CO₂ emissions in maritime shipping.
• Slow and computationally expensive route planning methods.
• Lack of real-time, safe, and energy-efficient vessel routing solutions.
• Dependence on expensive simulators for reinforcement learning in routing.
• Challenges balancing multiple objectives including safety and environmental impact.

Market Applications:

• Voyage optimization platforms for shipping companies.
• Integrated navigation solutions for vessel operators.
• Maritime insurers assessing and managing vessel risk.
• Classification societies promoting safety and environmental standards.
• Port and fleet management systems seeking efficiency improvements.
• Maritime software providers enhancing routing capabilities.