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海事环境下，能够执行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，以简化不同水面航行器平台间的自主能力迁移。这种数据驱动方法缓解了因系统行为演变或未预见的动态环境影响而导致先验系统模型失效的问题。所提出的学习平台包含深度库普曼系统模型与变化点检测器，后者可识别领域偏移并触发在严重外源与内源扰动下的重学习过程。通过最优控制器设计实现自主系统的运动控制，其中基于库普曼线性化模型可自然适配线性二次型调节器（LQR）控制框架。我们提出了C3D控制架构——融合变化点检测与深度库普曼学习的级联控制。该框架在自主水面航行器的位置保持任务中通过仿真与真实实验验证。在所有测试场景中，相较于未考虑系统变化的方法，本方法平均距离误差至少降低了13.9%。