Estimators for anticipating river meanders: a comparative study

Increasingly, marine robots and unmanned surface vehicles will be deployed in rivers and riverine environments. The structure produced by flowing waters is worth investigating because it may be exploited for purposes of estimation, planning, and control. This report adopts a widely acknowledged model for the geometry of watercourse channels, namely sine-generated curves, as a basis for estimators that predict the shape of the yet unseen portion of the river. Predictions of this sort help a robot to choose actions which anticipate the future, for example, in throttling speeds to round a bend. After examining how to reparameterize standard filters to incorporate this model, we compare the performance of three Gaussian filters and show that nonideality and theoretical challenges (of non-linearity, multi-modality/periodicity) degrade the performance of standard Kalman filters very severely, but can be successfully mitigated by imposing an interval constraint. Thereafter, we present results of a constrained interval Kalman filter on data from three natural rivers. The results we report, including data from simulation, on map-based data, and on GPS positions collected from a boat on the Colorado river, show the effectiveness of our method on the estimation of meander parameters. ∗Kai Qin ([email protected]) is with NXP Semiconductors. †Dylan A. Shell ([email protected]) is with the Department of Computer Science and Engineering, Texas A&M University.