论文标题
安全保证的轨迹规划和控制基于无人管表船的GP估计
Safety-guaranteed trajectory planning and control based on GP estimation for unmanned surface vessels
论文作者
论文摘要
我们建议使用高斯工艺（GPS）学习不受欢迎的表面船（USV）的安全保证计划和控制框架，以学习不确定性。 USV遇到的不确定性，包括外部干扰和模型不匹配，可能是状态依赖性的，时间变化的，并且很难用恒定的模型捕获。 GP是一种强大的基于学习的工具，可以与基于模型的计划和控制框架集成，该框架采用了汉密尔顿 - 雅各比差异游戏公式。这样的组合产生了较少的保守轨迹和保证控制策略。我们在ClearPath Heron USV上展示了拟议的框架和实验。
We propose a safety-guaranteed planning and control framework for unmanned surface vessels (USVs), using Gaussian processes (GPs) to learn uncertainties. The uncertainties encountered by USVs, including external disturbances and model mismatches, are potentially state-dependent, time-varying, and hard to capture with constant models. GP is a powerful learning-based tool that can be integrated with a model-based planning and control framework, which employs a Hamilton-Jacobi differential game formulation. Such a combination yields less conservative trajectories and safety-guaranteeing control strategies. We demonstrate the proposed framework in simulations and experiments on a CLEARPATH Heron USV.
