Receding horizon estimation and control with structured noise blocking for mobile robot slip compensation

TL;DR

This paper introduces a structured noise blocking approach within receding horizon estimation and control to improve slip compensation for mobile robots navigating uncertain terrains, enhancing estimation accuracy.

Contribution

It proposes a novel structured blocking method that relaxes the constant slip assumption in RHE, improving state and parameter estimation for off-road robot navigation.

Findings

Improved slip estimation accuracy in simulation

Enhanced control performance under varying terrain conditions

Overlapping-block strategy mitigates noise-block limitations

Abstract

The control of field robots in varying and uncertain terrain conditions presents a challenge for autonomous navigation. Online estimation of the wheel-terrain slip characteristics is essential for generating the accurate control predictions necessary for tracking trajectories in off-road environments. Receding horizon estimation (RHE) provides a powerful framework for constrained estimation, and when combined with receding horizon control (RHC), yields an adaptive optimisation-based control method. Presently, such methods assume slip to be constant over the estimation horizon, while our proposed structured blocking approach relaxes this assumption, resulting in improved state and parameter estimation. We demonstrate and compare the performance of this method in simulation, and propose an overlapping-block strategy to ameliorate some of the limitations encountered in applying noise-blocking in a receding horizon estimation and control (RHEC) context.