Multisensorial Self-Localization of an Autonomous Mobile Robot over Uneven Terrain

本論文旨在發展自動導航車在起伏之立體空間(3D)自我定位方法學與其技術。新式室內動態空間之導航車3D自我定位技術是分別應用增廣型卡爾曼濾波策略(Extended Kalman filtering)融合3D方位推算值(DR)與新式的三度空間超音波系統飛行時間量測值或結合3D慣性導航系統(INS)與三度空間超音波系統飛行時間量測值，產生高精確的姿態角與位置估測值。室外3D自我定位技術是應用卡爾曼濾波策略(Kalman filtering)融合3D慣性導航系統與差分式全球定位系統(DGPS)來實現自動導航車之高可靠度與高精確度的絕對位置與車頭方向及仰角估測值。三種定位技術可用以消除自動導航車因車輪打滑或近似的方位推算法則所導致的累加性誤差值或3D慣性導航系統的積分過程所造成的積分誤差。實驗設備採用兩車輪編碼器、三軸加速度計、地磁羅盤、水平傾斜儀、兩轉速迴轉儀、3D超音波系統與差分式全球定位系統，應用電路設計技術，介面技術及C++程式撰寫技巧，完成自我定位的軟硬體功能，其實驗數據足以說明所提方法的可行性與有效性。This thesis develops methodologies and techniques for three-dimension (3D) self-localization of an intelligent autonomous mobile robot over uneven terrain. The self-localization system can be done by means of integrating the dead-reckoning (DR) subsystem with the novel ultrasonic localization subsystem or cooperating the inertial navigation system (INS) with the ultrasonic localization subsystem for indoor navigation purpose. In the outdoor environment, the INS and the differential global position system (DGPS), which are complementary in their advantages and disadvantages, are adopted for obtaining accurate position and orientation estimation of the robot. All the proposed methods aim at obtaining robust and reliable attitude and position estimates of the vehicle and eliminating the accumulation errors caused by wheel slippage and surface roughness of dead-reckoning algorithms or integrated errors of inertial navigation subsystem. Experimental setups consist of two encoders, a triaxial accelerometer, a compass, a tilt sensor, two gyros, a laboratory built 3D ultrasonic subsystem and a DGPS. Utilizing digital and analog circuit design approaches, interfacing techniques and C++ programming techniques, the proposed location system was implemented and tested. Experimental results show the feasibility and effectiveness of the proposed methods.Chapter 1 Introduction........................................ 1 1.1 Introduction1........................................... 1 1.2 Literature Review ...................................... 5 1.3 Contributions of the Thesis............................. 8 1.4 Organization of the Thesis..............................10 Chapter 2 Indoor Posture Determination by Fusion Ultrasonic and Dead-Reckoning Measurements.....................11 2.1 Introduction............................................11 2.2 Three-Dimensional (3D) Dead-Reckoning Algorithm.........12 2.2.1 Robot Heading Estimate.............................13 2.2.2 Robot Pitch Estimate...............................15 2.2.3 Position Estimate..................................18 2.3 Integrating with the Ultrasonic Location System.........20 2.3.1 Geometric Configuration of the UltrasonicLocation System.............................................21 2.3.2 EKF-Based Sensor Fusion............................23 2.4 Experimental Results and Discussions....................28 2.4.1 The 3D DR System...................................28 2.4.2 The Integrated DR/Ultrasonic System................33 2.5 Concluding Remarks......................................43 Chapter 3 Indoor Self-Location Using Inertial and Ultrasonic Sensors.............................................45 3.1 Introduction............................................45 3.2 Rotational Transformation of Coordination...............47 3.2.1 Attitude Estimation................................47 3.2.2 Mathematical Formula of Coordination Transformation ...................................................48 3.3 INS System by Sensor Fusion.............................49 3.3.1 Accelerometer Transformation.......................49 3.3.2 Velocity Transformation............................50 3.3.3 Localization Estimation............................51 3.4 EKF Sensor Fusion for the INS and the Ultrasonic Localization System Integration.....................54 3.5 Experimental Results and Discussions....................57 3.5.1 INS Performance Evaluation.........................58 3.5.2 INS/Ultrasonics Experimental Results and Discussions........................................64 3.6 Concluding Remarks......................................67 Chapter 4 Outdoor Robot Positioning...........................69 4.1 Introduction............................................69 4.2 The DGPS System.........................................71 4.3 Kalman Filtering Algorithm for the Integrated INS/DGPS System..................................................75 4.4 INS/DGPS Experimental Results and Discussions...........76 4.5 Concluding Remarks......................................80 Chapter 5 Summaries and Recommendations.......................84 5.1 Summaries...............................................84 5.2 Recommendations.........................................85 Bibliography..................................................87 Appendix A....................................................91 Appendix B....................................................93 Appendix C....................................................9