Heterogeneous autonomous robot teams consisting of multirotor and uncrewed surface vessels (USVs) have the potential to enable various maritime applications, including advanced search-and-rescue operations. A critical requirement of these applications is the ability to land a multirotor on a USV for tasks such as recharging. This paper addresses the challenge of safely landing a multirotor on a cooperative USV in harsh open waters. To tackle this problem, we propose a novel sequential distributed model predictive control (MPC) scheme for cooperative multirotor-USV landing. Our approach combines standard tracking MPCs for the multirotor and USV with additional artificial intermediate goal locations. These artificial goals enable the robots to coordinate their cooperation without prior guidance. Each vehicle solves an individual optimization problem for both the artificial goal and an input that tracks it but only communicates the former to the other vehicle. The artificial goals are penalized by a suitable coupling cost. Furthermore, our proposed distributed MPC scheme utilizes a spatial-temporal wave model to coordinate in real-time a safer landing location and time the multirotor's landing to limit severe tilt of the USV.

翻译：异构自主机器人团队由多旋翼和无人水面艇（USV）组成，具有实现各类海事应用的潜力，包括先进的搜救作业。这些应用的关键需求之一是实现多旋翼在USV上的降落以完成充电等任务。本文针对恶劣开放水域中多旋翼安全降落在协同USV上的挑战。为解决该问题，我们提出了一种新颖的序贯分布式模型预测控制（MPC）方案，用于多旋翼-USV协同降落。该方法结合了多旋翼和USV的标准跟踪MPC，并引入额外的人工中间目标位置。这些人工目标使机器人无需先验引导即可协调合作。每台飞行器针对人工目标和追踪该目标的输入分别求解独立优化问题，但仅将前者通信至另一台飞行器。人工目标通过合适的耦合代价函数进行惩罚。此外，我们提出的分布式MPC方案利用时空波浪模型实时协调更安全的降落位置，并规划多旋翼降落时机以限制USV的严重倾斜。