Development of an Extended Kalman Filter Algorithm for the Localization of Underwater Mining Vehicles

J. Ocean Eng. Technol. 2005;19(2):82-89.

WON MOON-CHEOL;CHA HYUK-SANG;HONG SUP;

Chungnam National University;Chungnam National University;Ocean Development System Laboratory, KORDI;

해저 집광차량의 위치 추정을 위한 확장 칼만 필터 알고리즘

원문철;차혁상;홍섭;

충남대학교 메카트로닉스공학과;충남대학교 메카트로닉스공학과;한국해양연구원 해양시스템안전연구소 해양시스템연구본부;

Keywords: EKF(Extended Kalman Filter), Tracked Vehicle, Localization of Underwater Mining Vehicle

핵심용어: 확장 칼만 필터, 궤도차량, 해저주행차량 위치추정

Abstract

This study deals with the development of the extended Kalman filter(EKF) algorithm for the localization of underwater mining vehicles. Both simulation and experimental studies in a test bed are carried out. For the experiments, a scale dawn tracked vehicle is run in a soil bin containing cohesive soil of bentonite-water mixture. To develop the EKF algorithm, we use a kinematic model including the inner/outer track slips and the slip angle for the vehicle. The measurements include the inner and outer wheel speeds from encoders, the heading angle from a compass sensor and a fiber optic rate gyro, and x and y coordinate position values from a vision system. The vision sensor replaces the LBL(Long Base Line) sonar system used in the real underwater positioning situations. Artificial noise signals mimicking the real LBL noise signal are added to the vision sensor information. To know the mean slip values of the tracks in both straight and cornering maneuver, several trial running experiments are executed before applying the EKF algorithm. Experimental results show the effectiveness of the EKF algorithm in rejecting the sensor measurements noise. Also, the simulation and experimental results show close correlations.