Papers for

marine vehicle control teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Underwater robot navigates using flow history from single sensor

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

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.

Tue 22 SeptRobotics
The gist
Underwater robots usually need multiple sensors spread out to measure water flow and help them navigate, which can be tricky because of size limits. The authors show that by using past measurements from just one sensor, a robot can estimate important water flow information almost as well as having three sensors. They tested their method in computer simulations of water flow behind different obstacles and found it worked well across various conditions without retraining. This approach could simplify sensor design while still helping underwater robots navigate effectively.
Open 2609.26753v1