A robust slip estimation method for skid-steered mobile robots

Xiaojing Song, Lakmal D. Seneviratne, Kaspar Althoefer, Zibin Song

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

This paper presents a robust slip estimation method for skid-steered mobile robots when they traverse over rough terrain. An optical flow-based visual sensor looking down the terrain surface is employed to recover motion of a mobile robot by tracking features selected from the terrain surface. The motion states of the mobile robot are initially estimated by the visual sensor, however, the estimates are prone to noise and uncertainty which degrades the accuracy and robustness of estimation. To cope with the noise and uncertainty from the visual sensor, a sliding mode observer (SMO) based on the kinematics model of the skid-steered mobile robot is delicately designed to simultaneously estimate slip parameters. The SMO scheme can give more accurate estimates than the extended Kalman filter (EKF) when the slip of the mobile robot has significant changes at abrupt steering. The complete slip estimation method is independent of terrain parameters and robust in the presence of noise and uncertainty. Experimental results show that the method has confident potential for slip estimation of skid-steered mobile robots.

Original languageBritish English
Title of host publication2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008
Pages279-284
Number of pages6
DOIs
StatePublished - 2008
Event2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008 - Hanoi, Viet Nam
Duration: 17 Dec 200820 Dec 2008

Publication series

Name2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008

Conference

Conference2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008
Country/TerritoryViet Nam
CityHanoi
Period17/12/0820/12/08

Keywords

  • Optical flow
  • Skid-steered mobile robot
  • Sliding mode observer
  • Slip estimation

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