Incremental Evaluation of Distance-Staged Curriculum Learning and LiDAR-Based Runtime Shielding for USV Navigation in Constrained Nearshore Waters
Year: 2026
Type: article
Abstract: Autonomous navigation in constrained nearshore waters is challenged by boundary constraints, static obstacles, and incomplete perception. Using Proximal Policy Optimization (PPO) as a common baseline, this study integrates distance-staged curriculum learning and a LiDAR-based Safety Shield into a point-to-point USV obstacle-avoidance framework and evaluates their incremental effects on training-task organization and runtime action filtering. Validation includes fixed-stratified paired tests, unseen obstacle layouts, three independent training runs, threshold-sensitivity analysis, and new cross-seed Shield evaluation on independently trained policies. Curriculum learning does not provide a consistent final success-rate advantage across the three training runs; its paired decision-efficiency effect is statistically detectable in the original run but practically modest. By contrast, the Safety Shield increases success and reduces collisions across all four independently trained seed-101/202 policies tested with Shield OFF/ON, while introducing a small number of timeouts and a small efficiency cost in triggered cases. Controlled runtime diagnostics further show that the Shield’s slow-forward command is a reduced-propulsion action rather than explicit braking. Overall, distance staging mainly changes training-task organization and produces modest decision-efficiency effects, whereas runtime shielding exhibits a reproducible trade-off between collision-risk mitigation and intervention cost within the present simplified Unity environment.
DOI: 10.3390/jmse14212020
Authors: Siliang Pang, Pengpeng Wan, Yihao Ye, Jialin Wang
Institution: Naval University of Engineering
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7223956923