用强化采样优化路径，实现无人船精准自动靠泊
Model Predictive Path Integral Docking of Fully Actuated Surface Vessel
- 结合概率轨迹优化与多目标成本函数，动态生成最优靠泊路径
- 在多种初始位置下成功靠泊，保持安全距离与平滑运动
- 基于实时激光雷达检测更新靠泊参数，适合复杂水域应用
自主靠泊是海洋机器人领域最具挑战性的操作之一，需在狭小空间内实现精确控制与鲁棒感知。本文提出一种新方法，将模型预测路径积分（MPPI）控制与基于实时激光雷达的靠泊检测相结合，用于无人水面艇的自主靠泊。该框架融合概率轨迹优化与多目标代价函数，同时考虑靠泊精度、安全约束和运动效率。MPPI控制器通过智能采样控制序列并根据动态避碰要求、姿态对齐和目标位置目标评估其代价，生成最优轨迹。我们引入自适应靠泊检测流程，处理激光雷达点云以提取关键几何特征，实现靠泊参数的实时更新。所提方法在包含真实传感器噪声、船舶动力学和环境约束的物理仿真环境中进行了广泛验证。结果表明，无论从何种初始位置出发，系统均能成功完成靠泊，且保持安全间距与平滑运动特性。
原文摘要 · Abstract (English)
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.
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