发表机构
Iowa State University; Brown University（爱荷华州立大学; 布朗大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用单一传感单元的测量历史，通过因果观测器估计横向流速，实现非定常流中的水下导航，在多种工况下接近直接空间传感性能。
AI 中文摘要
空间流场测量支持水下导航，但分布式传感受限于机器人尺寸和传感器布局。我们使用一个因果观测器，从单一传感单元收集的有限测量历史中估计横向流速，为固定导航控制器提供输入。在可获取机体坐标系环境速度的二维尾流模拟中，该虚拟传感接口将同时流场采样从三个点减少到机器人中心。仅在Re=100的圆柱尾流中训练的流历史观测器，在未重新训练的Re=205和240留出工况下分别达到84.4%和80.6%的成功率。这些比率比直接空间传感低7.4和4.6个百分点，但比匹配的仅电流观测器高30个百分点以上。当过去的目标和偏航信息可用时，过去的流场信息仍然有益。在不同障碍物几何形状下，方形棱柱尾流中的性能接近直接传感，但在三角形棱柱尾流中有所下降。组件替换识别出横向速度差为控制相关因素，而受控扰动揭示了误差持续性的敏感性。结果证明了在所假设的观测模型下，单点流历史的闭环实用性。
英文摘要
Spatial flow measurements support underwater navigation, but distributed sensing is constrained by robot size and sensor layout. We use a causal observer to estimate current lateral velocities from a finite history of measurements collected by a single sensing unit, supplying the inputs of a fixed navigation controller. In two-dimensional wake simulations with access to body-frame ambient velocity, this virtual sensing interface reduces simultaneous flow sampling from three points to the robot center. Trained only in a circular-cylinder wake at Re = 100, the flow-history observer achieves 84.4% and 80.6% success at held-out Re = 205 and 240 without retraining. These rates are 7.4 and 4.6 percentage points below direct spatial sensing and more than 30 points above a matched current-only observer. Past flow remains beneficial when past goal and yaw information is available. Across obstacle geometries, performance remains close to direct sensing in square-prism wakes but declines in triangular-prism wakes. Component replacement identifies the lateral velocity difference as control-relevant, while controlled perturbations reveal sensitivity to error persistence. The results demonstrate the closed-loop utility of single-point flow histories under the assumed observation model.
CommentsThis paper is under review at the IEEE International Conference on Robotics and Automation (ICRA)
