モデル予測パス積分による全駆動型水上船舶の自動ドッキング

原文アブストラクト

Autonomous docking remains one of the most challenging maneuvers in marine robotics, requiring precise control and robust perception in confined spaces. This paper presents a novel approach integrating Model Predictive Path Integral(MPPI) control with real-time LiDAR-based dock detection for autonomous surface vessel docking. Our framework uniquely combines probabilistic trajectory optimization with a multiobjective cost function that simultaneously considers docking precision, safety constraints, and motion efficiency. The MPPI controller generates optimal trajectories by intelligently sampling control sequences and evaluating their costs based on dynamic clearance requirements, orientation alignment, and target position objectives. We introduce an adaptive dock detection pipeline that processes LiDAR point clouds to extract critical geometric features, enabling real-time updates of docking parameters. The proposed method is extensively validated in a physics-based simulation environment that incorporates realistic sensor noise, vessel dynamics, and environmental constraints. Results demonstrate successful docking from various initial positions while maintaining safe clearances and smooth motion characteristics.