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MPPI planning helps unmanned boats search for and capture drifting targets
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Sanjeev Ramkumar Sudha and other authors published a paper proposing a spatiotemporal information planning method based on model predictive path integral (MPPI) control for unmanned surface vehicles to search for and capture multiple drifting targets. The planner couples target drift prediction with the exploration objective through cost design, optimizes continuous trajectories over a long horizon, and directly generates kinematic commands; in the interception phase, pure pursuit guidance is used to achieve physical capture. The authors report that in simulation, the method captured 18% more targets than the strongest baseline, and the method was validated in real-boat experiments.
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- MPPI-Based Trajectory Planning for Drifting Target Search and Capture with Unmanned Surface Vehicles
This study proposes a spatiotemporal informative planning method based on model predictive path integral (MPPI) control for unmanned surface vehicles (ASVs) to search for and capture multiple drifting targets. The planner couples target drift prediction with exploration objectives through a cost design, optimizes continuous trajectories over long horizons, and directly generates kinematic commands; in the interception stage, pure pursuit guidance is used to achieve physical capture. In simulation, the method captured 18% more targets than the strongest baseline, and the approach was validated in real-boat trials.
arXiv cs.RO ·