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研究生： 陳子靖
Chen, Tzu-Ching
論文名稱： 四輪驅動自駕賽車之同步定位與地圖建置、導航控制及扭力引導系統研究
SLAM, Navigation and Torque Vectoring for a AWD Autonomous Racing Vehicle
指導教授： 葉廷仁
Yeh, Ting-Jen
口試委員: 洪建中
Hong, Chien-Chong
顏炳郎
Yen, Ping-Lang
學位類別： 碩士
Master
系所名稱： 工學院 - 動力機械工程學系
Department of Power Mechanical Engineering
論文出版年： 2022
畢業學年度： 110
語文別： 英文
論文頁數： 65
中文關鍵詞： 四輪獨立驅動 、自動駕駛 、模型預估控制 、隨機最佳化控制 、卡爾曼濾波器 、同步定位與地圖建置 、扭力引導系統 、圖形處理器 、平行運算
外文關鍵詞： All-wheel Drive (AWD), Autonomous Driving System (ADS), Model Predictive Control (MPC), Stochastic Optimization, Kalman Filter, Simultaneous Localization and Mapping (SLAM), Torque Vectoring, GPU, Parallel Computing
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    • 本研究基於學生方程式無人賽車競賽，使用縮小比例實驗載具在角錐定義的賽道中實現環境感知、即時定位與地圖建構、路線建立、隨機模型預測最佳化控制。整台自駕車的運算架構可大致分為感測器融合與導航控制兩部分。感測器融合利用擴展式卡爾曼濾波器與FastSLAM-1.0演算法，融合相機與各項感測器資訊，以估測出車輛位置和角錐地圖等資訊。導航控制則是基於定位和路徑資料，應用圖形處理器平行運算多個車輛動力學模型，以模型預測控制-隨機最佳化方式求解車輛之控制，並引入扭力引導系統(Torque Vectoring)以輔助車輛達成更高的偏航角速度，以減緩轉向不足，使車輛過彎更加靈活。

    Based on Formula Student Driverless (FSD) competition, this research uses a scale-down prototype vehicle to achieve environment perception, simultaneous localization and mapping (SLAM), road building, and stochastic model predictive control (MPC) in the track defined by the cones. The computing structure of the autonomous vehicle can be divided into two parts: sensor fusion and navigation control. Sensor fusion applies extended Kalman filter (EKF) and FastSLAM-1.0 algorithms to estimate vehicle position and coned map using measurements from two cameras and various sensors. Navigation control is based on localization results and map information, and it relies on the graphics processing unit (GPU) to calculate multiple vehicle trajectories in parallel for solving the vehicle control problem by stochastic MPC. The navigation control also introduces torque vectoring to further enhance the vehicle performance. The understeer phenomenon is alleviated and a higher yaw rate is achieved, which makes the vehicle more agile in cornering.

    1 Introduction 1 1.1 Overview of Autonomous Driving Technology 1 1.2 Motivation 1 1.3 Related Work 3 1.4 Thesis Outline 6 2 System Overview and Vehicle Models 7 2.1 Driving Scenario Design 8 2.2 AWD Electric Vehicle（QRacer） 9 2.3 Vehicle Model 15 2.3.1 Dynamic Model 18 2.3.2 Kinematic Model 18 2.3.3 Nonlinear Tire Force Model 19 2.3.4 No-slip Kinematic Model 20 2.3.5 Electric Steering and Effective Steering Angle 20 2.3.6 Torque Vectoring 23 3 State Estimation 25 3.1 Extended Kalman Filter (EKF) 26 3.2 Simultaneous Localization and Mapping (SLAM) 28 3.3 Vehicle States Transition and Timing 30 4 Road Builder 32 4.1 Delaunay Triangulation 32 4.2 Optimal Triangle Branch Search 33 4.3 Grid Map Generation 36 4.4 Road Builder Algorithm 36 5 Navigation & Dynamic Control 37 5.1 Dynamic Model & Tire Model Identification 37 5.2 Stochastic Model Predictive Control (SMPC) 40 6 Experiment Results 45 6.1 Torque Vectoring Control 45 6.1.1 Approach of Torque Vectoring Control 45 6.1.2 Validation of Torque Vectoring Control 47 6.2 Indoor Test In Unknown Map 49 6.3 Outdoor Test 51 6.3.1 Track Setup 51 6.3.2 Tire Fitting and Ground Speed Sensor Checking 51 6.3.3 Outdoor Test in Unknown Map 54 7 Conclusion and Future Work 56 7.1 Improvement of the SLAM Performance 56 7.2 Rules for Entering Localization Mode 56 7.3 Adding Yaw Moment into MPC Algorithm 57 Appendix 58 A. Down-scale Dimension Analysis 58 B. Planar Quasi Steady State Vehicle Dynamics 59 Reference 61

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