해저 집광차량의 위치 추정을 위한 확장 칼만 필터 알고리즘

다국어 초록 (Multilingual 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 down 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.

참고문헌 (Reference)

1 "직교행렬 실험계획법에 의한 해저연약지반 선회성능실험 연구" 2003

2 "Computing Integrals Involving the Matrix Exponential" ac-23 : -3, 1978

3 "Combined Doppler / LBL Based Navigation of Underwater Vehicles 11th International Symposium on Untethered Submersile Technology" 1999

4 ". Modelling and Control of Tracked Vehicles" 1999

5 ". Introduction to Random Signals and Applied Kalman Filtering" 1997