臺灣博碩士論文加值系統

本論文旨在利用車輛模型與粒子濾波器追蹤演算法發展自主式駕駛應用。一般來說，車輛導航系統包括即時環境感知，車輛定位，車輛避撞，路徑規劃以及路徑追隨控制。為了實現及開發智能自走車功能，本研究整合慣性感測元件（IMU），GNSS接收機和增量式編碼器進行載具動態估測。在演算法設計過程中，地圖輔助的路徑規劃提供一個追蹤之參考路線。
為此，開發於LabVIEW平台之人機界面（UMI）即可進行即時觀察路徑追蹤情況。在車輛預測控制的方面，本文利用粒子濾波器演算法於規劃軌跡上作路徑預測。遞迴式粒子濾波器以取樣權重估測位置並且推估轉向角響應。另外，所有應用之感測器皆整合至一個嵌入式運算平台並進行即時接收以及效能評估。搭配感測器以及嵌入式電腦平台之輕型電動車於校園內進行實驗並且路徑預測與轉向角之性能也由此驗證。

The thesis describes the design, implementation, and test of an autonomous vehicle navigation system using vehicle model and particle filter tracking algorithm. Typically, a vehicle navigation system comprises of real-time environment perception, vehicle localization, collision avoidance, path planning, and path following control. In order to implement the features for intelligent autonomous vehicle, a sensor suite of integrated inertial measurement unit (IMU), GNSS receiver, and incremental encoder is developed for vehicle dynamic estimation. In the design, a map-aided path planning strategy is employed to generate a reference route. To this end, a UMI (User Machine Interface) programming on LabVIEW platform is developed to facilitate the observation of a goal-oriented path tracking performance.
In the aspect of vehicle estimation control, the system utilizes particle filter algorithm to ensure that the actual trajectory follows the planned path. The recursive particle filter is able to weight the cells and response the angle as well as estimated position information. All the sensors are integrated into an embedded computer platform to assess the autonomous driving capability. The test is conducted on campus by installing the sensor suite and embedded computer platform into an electric vehicle. The trajectory tracking capability is preliminarily verified.

摘要 I
Abstract II
Acknowledgements IV
Content VI
List of Tables IX
List of Figures X
List of Abbreviations XII
Chapter 1. Introduction 1
1.1 Motivation 1
1.2 Literature Review 4
1.3 Contributions of the Thesis 6
1.4 Organization 7
Chapter 2. Vehicle System and Modeling 8
2.1 Coordinate Systems 8
2.1.1 Earth-Fixed Axis System 9
2.1.2 Vehicle Axis System 10
2.1.3 Local ENU (East-North-Up) Coordinate System 11
2.1.4 Coordinate Transformation 12
2.2 Modeling of Vehicle Dynamics 17
2.2.1 Kinematic Model 17
2.2.2 Dynamic Model 19
2.2.3 Nonlinear Filter for Dynamic Modeling 24
Chapter 3. Particle Filter Tracking Algorithm 26
3.1 Bayes Filter 26
3.1.1 Bayes' Theorem 26
3.1.2 Markov Process Assumptions 28
3.1.3 Bayes Filter Implementation 29
3.2 Classical Particle Filter 33
3.2.1 Basic Algorithm 33
3.2.2 Particle Filter Derivation 33
3.2.3 General Particle Filter Implementation 35
3.3 Vehicle Motion and Control 37
3.3.1 Programming Flow 37
3.3.2 Map Generation 39
3.3.3 Initialization 42
3.3.4 Resampling 44
3.3.5 Estimation Result 46
Chapter 4. System Implementation and Experiments 48
4.1 System Architecture 48
4.2 Mechatronic system 49
4.2.1 Embedded Controller 50
4.2.2 I/O Modules and Sensor Modules 53
4.3 Experiment Result 56
4.4 Human Machine Interface 64
4.4.1 Position Estimation Result 64
4.4.2 IMU Information 66
4.4.3 Orientation Information 68
4.4.4 Particle Filter Estimation 69
4.4.5 Steering Control Panel 71
Chapter 5. Conclusions and Future Work 73
5.1 Summary of Results 73
5.2 Future Research 74
Reference 76

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