Underwater Navigation in Unsteady Flows Using Measurement Histories from a Single Sensing Unit

2026-09-22

AI Summary

研究使用单个传感器的历史测量数据估计流速，支持水下导航，减少对多点采样的依赖，提高在不同流场中的导航成功率。

Abstract

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.

Problem

• Underwater Navigation
• Unsteady Flows
• Single Sensing Unit
• Measurement Histories
• Spatial Flow Measurements

Innovation

• causal observer
• single sensing unit
• flow history
• underwater navigation
• lateral velocity estimation