Localization of Biomimetic Autonomous Underwater Vehicle by Environmental Detection

本文提出一個使用延伸式卡爾曼濾波器的資訊融合方法，裝設於仿生型自主式水下載具（簡稱BAUV）上做為環境偵測與導航之用。本文於BAUV載具前端裝設聲納以及攝影機來偵測未知的環境，單點式聲納可以提供載具與未知物的距離，而立體視覺也可配合載具狀態來計算在影像座標上目標物與載具之間的相對位置，聲納及攝影機之資料先由卡爾曼濾波器執行載具及目標點位置估測，二者之資訊再由聯邦濾波器來更新估測值，使得載具在感測器資訊融合之下能夠準確地的接近目標點。本文所提出的資料融合方法，最後以BUAV載具在水槽內測試可行性，實驗結果證明本方法皆可以提高載具及環境位置估測之準確度。A sensor fusion algorithm using Extended Kalman Filter for a fishlike Biomimetic Autonomous Underwater Vehicle (BAUV) is presented. This is a step toward the applications of BAUVs in real missions. There are six brushless DC servomotors mounted inside the BAUV, four of the motors responsible for the motion of side fins, two motors responsible for the tail joint and the tail fin joint. In addition, the BAUV’s head moves in a cyclic fashion, so one can use an echo sounder and two video cameras installed on its head to detect the environment. Firstly, BAUV uses an imaging sonar to detect and estimate the target location roughly until the BAUV proceeds to visible range of the target, then switch to visual guidance mode to reach the target point in good precision. An efficient, federated Kalman filter is developed for use in distributed multi-sensor systems. Using data fusion and updating the information, the BAUV can have more accuracy in detecting target and control its orientation to approach targets in an unknown environment.誌謝 Ⅰ 摘要 Ⅱ ABSTRACT Ⅲ TABLE OF CONTENTS Ⅳ LIST OF FIGURES Ⅵ LIST OF TABLES Ⅷ LIST OF SYMBOLS Ⅸ CHAPTER1 INTRODUCTION 1 1.1 MOTIVATION 3 1.2 RELATED WORK 3 1.3 THESIS ORGANIZATION 5 CHAPTER2 MODELING OF UNCERTAINTY 6 2.1 COORDINATE SYSTEMS 6 2.2 MOTION UNCERTAINTY 7 2.3 MATHMATICAL MODEL OF OBSERVATIONAL UNCERTAINTY 9 2.3.1 Stereo Image 9 2.3.2 Uncertainty Model from Stereo Image 11 2.3.3 Sonar System 13 2.3.4 Uncertainty Model from Echo Sounder 15 2.4 HOUGH TRANSFORM 16 CHAPTER3 MEASUEMENTS AND DATA FUSION 17 3.1 OBSERVATION EQUATION 17 3.2 ESTIMATING STATE AND COVARIANCE BY EKF 19 3.3 FEDERATED FILTER 22 3.4 WAYPOINT-TRACKING CONTROL SYSTEM 25 3.4.1 Controller Design Concept 26 3.4.2 The Local Control 27 3.4.3 The Global Control 27 3.5 SIMULATIONS 29 CHAPTER4 EXPERIMENTS 34 4.1 DESCRIPTION OF HARDWARE 34 4.2 CALIBRATION AND THE ERROR OF SENSORS 37 4.2.1 Camera Calibration 37 4.2.2 Quantization Error 41 4.2.3 The Error of Echo Sounder 44 4.3 EXPERIMENT 45 CHAPTER5 CONCLUDING REMARKS 57 REFERENCES 5