Experimental validation of structured receding horizon estimation and control for mobile ground robot slip compensation

EasyChair Preprint 1458

Abstract

To achieve high accuracy tracking performance for wheeled mobile robots in spatially varying terrain conditions, it is necessary to estimate both the robot's state and the slip conditions of the environment to a high degree of precision. The receding horizon estimation and control (RHEC) framework presents a systematic, adaptive optimisation approach to this problem, to which our prior work proposed a structured blocking (SB) extension to address performance limitations for motion both at high speeds and over varying terrain. In this work, we validate these results in a series of preliminary field experiments with the Swagbot platform, demonstrating performance improvements in position tracking of up to 7%, and up to 13% for speed tracking at speeds of 1.5 and 2.5 m/s.

Keyphrases: Autonomous Vehicle Navigation, Field Robots, Model Predictive Control, Nonlinear Model Predictive Control, Robotics in Agriculture and Forestry, Wheeled Robots, agricultural automation, autonomous agents, motion control, moving horizon estimation, nonlinear receding horizon estimation, path tracking, receding horizon control, receding horizon estimation

@booklet{EasyChair:1458,
  author    = {Nathan Wallace and He Kong and Andrew Hill and Salah Sukkarieh},
  title     = {Experimental validation of structured receding horizon estimation and control for mobile ground robot slip compensation},
  howpublished = {EasyChair Preprint 1458},
  year      = {EasyChair, 2019}}