H-SPAR: Hydrodynamic-aware Simulation for Particle Transport and Autonomous Robots

2026-10-01

AI Summary

This study addresses the limitation of existing marine robotic simulations, where decoupled modeling of ocean currents, autonomous control, and sampling prevents holistic task performance evaluation. To overcome this, we develop a closed-loop simulation platform integrating time-varying flow fields, Lagrangian particle transport, and ROS 2/Gazebo-based unmanned surface vessel control. Innovatively, a shared precomputed flow field synchronously drives both particle advection and hydrodynamic forces on the vessel. By combining SVF-RRT* path planning with probabilistic sampling algorithms, the framework enables end-to-end joint evaluation of planning, execution, and sampling under consistent hydrodynamic conditions. Results demonstrate that flow-aware planning significantly reduces navigation costs and reveal the degradation pattern of execution gains caused by time-varying currents. Furthermore, the impact of sweep direction on sampling efficiency is quantitatively characterized.

Abstract

Environmental robotic sampling requires considering the dual influence of water currents on robotic motion and particle transport. Existing marine robotics simulators generally model flow, autonomy, and sampling targets separately, limiting joint evaluation of mission cost and sampling performance. H-SPAR integrates spatially and temporally varying velocity fields, Lagrangian particle transport, probabilistic sampling, and ROS 2/Gazebo-based uncrewed surface vehicle (USV) autonomy. In this work, shared precomputed flow fields drive particle advection and current-induced forces during closed-loop vehicle execution. Path-planning experiments show that the existing current-aware planner SVF-RRT* achieves 69.4% lower upstream cost than conventional RRT* at the planning level, but this reduction falls to 41.7% during execution under time-varying currents, reflecting temporal flow variation, vehicle motion constraints, and path deviation omitted during planning. Coverage experiments show that sweep orientation changes the particle-sampling rate by up to 22.2% under the complete H-SPAR configuration. These findings highlight the importance of evaluating planning, vehicle execution, particle transport, and sampling together under consistent hydrodynamic conditions. The project webpage is available at https://sites.google.com/view/h-spar, and the open-source code is available on GitHub at https://github.com/naviiidz/h-spar-sim.

Problem

• marine robotics simulation
• environmental sampling
• hydrodynamic-aware planning
• particle transport
• uncrewed surface vehicle

Innovation

• Hydrodynamic-aware simulation
• Lagrangian particle transport
• Autonomous surface vehicle
• ROS 2/Gazebo integration
• Current-aware path planning