When deploying autonomous systems in unknown and changing environments, it is critical that their motion planning and control algorithms are computationally efficient and can be reapplied online in real time, whilst providing theoretical safety guarantees in the presence of disturbances. The satisfaction of these objectives becomes more challenging when considering time-varying dynamics and disturbances, which arise in real-world contexts. We develop methods with the potential to address these issues by applying an offline-computed safety guaranteeing controller on a physical system, to track a virtual system that evolves through a trajectory that is replanned online, accounting for constraints updated online. The first method we propose is designed for general time-varying systems over a finite horizon. Our second method overcomes the finite horizon restriction for periodic systems. We simulate our algorithms on a case study of an autonomous underwater vehicle subject to wave disturbances.

翻译：在未知且动态变化的环境中部署自主系统时，其运动规划与控制算法需满足计算高效性、支持在线实时重规划，并在存在扰动的情况下提供理论安全保证。当考虑实际场景中普遍存在的时变动力学与扰动时，这些目标的实现更具挑战性。本文通过将离线计算的安全保证控制器应用于物理系统，使其跟踪一个在线重规划轨迹的虚拟系统，并在线更新约束条件，提出了有望解决上述问题的方法。我们提出的第一种方法针对有限时域内的通用时变系统设计；第二种方法则突破了周期系统的有限时域限制。通过自主水下航行器受波浪扰动的案例研究，我们对所提算法进行了仿真验证。