ACA-LOS: Path-Normal Current-Aware Adaptive Guidance for Coverage-Lane Keeping of Unmanned Surface Vehicles

Abstract

To address sustained deviation from coverage lanes when an unmanned surface vehicle (USV) executes a complete boustrophedon coverage path under current disturbances, this study proposes an adaptive current-aware line-of-sight (ACA-LOS) guidance method based on online current estimation. The local current is estimated online from the kinematic velocity relationship between the USV and the surrounding water, and its path-normal component is extracted. The estimated path-normal current is used both to construct a current-compensating heading correction and, together with the cross-track error, to adjust the LOS lookahead distance and generate the desired ACA-LOS heading. Complete-path simulations are conducted under constant, time-varying, and spatially varying currents with different current speeds and directions. Relative to conventional LOS, ACA-LOS reduces the mean cross-track root mean square error (RMSE) by 40.80%, increases the tolerance-band compliance rate (Rb) by 27.48 percentage points, and reduces the cumulative cross-track deviation area (Ad) by 68.68%. ACA-LOS suppresses sustained lane deviation caused by current disturbances and improves lane keeping throughout complete boustrophedon path execution. The method supports the extension of USV operation from single-trajectory tracking to long-duration, continuous coverage execution and provides a reference for unmanned and intelligent waterborne operational equipment.