We present a scalable combined localization infrastructure deployment and task planning algorithm for underwater assembly. Infrastructure is autonomously modified to suit the needs of manipulation tasks based on an uncertainty model on the infrastructure's positional accuracy. Our uncertainty model can be combined with the noise characteristics from multiple devices. For the task planning problem, we propose a layer-based clustering approach that completes the manipulation tasks one cluster at a time. We employ movable visual fiducial markers as infrastructure and an autonomous underwater vehicle (AUV) for manipulation tasks. The proposed task planning algorithm is computationally simple, and we implement it on AUV without any offline computation requirements. Combined hardware experiments and simulations over large datasets show that the proposed technique is scalable to large areas.

翻译：我们提出了一种用于水下装配的可扩展联合定位基础设施部署与任务规划算法。该基础设施基于位置精度不确定性模型自主调整以适应操作任务需求，所提出的不确定性模型可与多种设备的噪声特性相结合。针对任务规划问题，我们提出了一种基于层级聚类的算法，每次完成一个聚类子集的操作任务。采用可移动视觉基准标记作为基础设施，并利用自主水下航行器（AUV）执行操作任务。所提出的任务规划算法计算复杂度低，无需离线计算即可在AUV上实现。结合硬件实验与大规模数据集仿真表明，该技术具有良好的大区域可扩展性。