Evaluating Hybrid and Sampling-Based Path Planners for Unmanned Surface Vehicle Logistics in Dynamic Inland Waterways

Autonomous surface vehicles (ASVs), particularly unmanned surface vehicles (USVs), play an increasingly important role in supporting logistics across Indonesia’s archipelagic and riverine regions, where shallow depths, strong currents, and debris complicate navigation. The research contribution is the development of a unified USV logistics simulation framework that enables systematic benchmarking of multiple path-planning strategies under realistic inland-waterway conditions. The framework integrates hydrodynamic environmental modeling, stochastic mission generation, and multi-planner evaluation within a single experimental loop. Nine algorithms were implemented, including classical planners (A*, D* Lite, GA, APF, Reactive Steering), sampling-based methods (RRT, RRT*), and hybrid variants (HARRT*, AHA-RRT*) that employ current-aware travel-time costs and adaptive replanning. Experiments conducted on a 200 × 200 m simulated domain with a fleet of three USVs under a 10-minute deadline revealed that sampling-based planners, particularly RRT*, achieved complete mission success with zero collisions, efficient energy use, and high obstacle clearance. The adaptive hybrid AHA-RRT* also performed robustly, showing superior adaptability albeit with longer paths due to frequent replans. In contrast, classical and reactive methods failed to meet time and safety constraints. These findings demonstrate that sampling-based and hybrid planners offer the most effective balance of safety, efficiency, and adaptability for autonomous inland-waterway logistics, providing a validated framework to guide future USV deployment in dynamic archipelagic environments.