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Time-optimal global path planning and collision-avoidance local path planning for USVs in traffic separation
1School of Marine Electrical Engineering, Dalian Maritime University, No.1, Linghai Road, Dalian, 116026, Liaoning, China.
ISA Transactions
|July 5, 2025
Summary
This study introduces a novel hierarchical Gaussian-process-based nonlinear programming (GPNLP) approach for unmanned surface vehicle (USV) path planning. The method ensures time-optimal global and collision-avoidance local paths while adhering to traffic separation schemes and USV dynamics.
Area of Science:
- Robotics
- Marine Engineering
- Artificial Intelligence
Background:
- Path planning for unmanned surface vehicles (USVs) in coastal waters with traffic separation schemes (TSS) is complex due to multiple constraints.
- Existing methods struggle to balance time-optimality, collision avoidance, and adherence to TSS requirements.
Purpose of the Study:
- To develop an innovative hierarchical approach for time-optimal global path planning and collision-avoidance (COLAV) local path planning for USVs.
- To effectively manage constraints including USV dynamics, TSS rules, navigable water boundaries, and collision risks.
Main Methods:
- A hierarchical Gaussian-process-based nonlinear programming (GPNLP) approach was developed.
- Gaussian process regression was used to model irregular static obstacles, optimizing navigable water utilization.
- A TSS compliance assessment function was integrated to penalize TSS violations.
Main Results:
- The proposed GPNLP approach successfully plans time-optimal global paths and COLAV local paths for USVs.
- The planned paths demonstrated compliance with USV dynamics and TSS requirements in simulations.
- Irregular obstacles were effectively modeled, enhancing navigable water usage.
Conclusions:
- The hierarchical GPNLP approach offers a robust solution for USV path planning in complex TSS-implemented waters.
- This method enhances safety and efficiency by ensuring dynamic compliance and adherence to traffic regulations.
- The innovative use of Gaussian process regression for obstacle modeling improves path planning outcomes.
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