In this paper, we discuss the development and deployment of a robust autonomous system capable of performing various tasks in the maritime domain under unknown dynamic conditions. We investigate a data-driven approach based on modular design for ease of transfer of autonomy across different maritime surface vessel platforms. The data-driven approach alleviates issues related to a priori identification of system models that may become deficient under evolving system behaviors or shifting, unanticipated, environmental influences. Our proposed learning-based platform comprises a deep Koopman system model and a change point detector that provides guidance on domain shifts prompting relearning under severe exogenous and endogenous perturbations. Motion control of the autonomous system is achieved via an optimal controller design. The Koopman linearized model naturally lends itself to a linear-quadratic regulator (LQR) control design. We propose the C3D control architecture Cascade Control with Change Point Detection and Deep Koopman Learning. The framework is verified in station keeping task on an ASV in both simulation and real experiments. The approach achieved at least 13.9 percent improvement in mean distance error in all test cases compared to the methods that do not consider system changes.

翻译：本文探讨了在未知动态条件下，能够执行海洋领域多种任务的鲁棒自主系统的开发与部署。我们研究了一种基于模块化设计的数据驱动方法，以简化自主能力在不同海洋水面航行器平台间的迁移。该数据驱动方法规避了系统模型先验辨识的难题——在系统行为演变或未预期的环境扰动下，先验模型可能失效。我们提出的学习型平台包含深度库普曼系统模型与变化点检测器，后者可指引领域漂移的方向，从而在外源与内源剧烈扰动下触发模型重新学习。自主系统的运动控制通过最优控制器设计实现，库普曼线性化模型自然适配线性二次型调节器控制设计。我们提出了C3D控制架构，即融合变化点检测与深度库普曼学习的级联控制。该框架在自主水面艇的定点保持任务中通过了仿真与真实实验验证。与未考虑系统变化的方法相比，本方法在所有测试案例中的平均距离误差至少降低13.9%。