本研究整合路徑規劃演算法與模型預測控制，開發出一套自動駕駛停車系統，
將Hybrid A*演算法與模型預測控制程式化，以ROS package的形式包裝於車控電腦Drive PX2中，本實驗所使用之車輛為工研院與中華汽車所開發的CPEV，將其車輛模型建置於車用模擬軟體PreScan之中，並透過ROS Network建立起windows與linux作業系統之間的通訊，由模擬軟體PreScan提供車輛狀態與環境數據給控制策略進行運算，經由控制器計算後將控制命令回傳至PreScan中，在兩系統之間建立路由。為了能測試路徑規劃演算之路徑可行性，以及模型預測控制器軌跡跟蹤的精確程度，本研究以模型迴路(Hardware-in-the-loop, HIL)進行整合的測試與驗證，為了增加模擬的真實性，串接方向盤、踏板等硬體設備，並設計一套手自動駕駛模式切換系統，讓駕駛者自由切換駕駛模式，並撰寫使用者介面，將停車場地圖顯示於RVIZ中，讓駕駛者能選擇欲停的車位。在避障策略中，透過RADAR感測器，遇到障礙物時採用緊急煞停，更新地圖並從新規劃路徑。本研究實現了將自動駕駛上層控制與下層控制，進行三種不同的情境下的實驗分別為正向停車、倒車入庫、避障停車，為了讓此系統更符合真實的行車結果，模擬的情境也根據真實的場域來建置。本研究完成自動駕駛模擬的軟硬體整合，並開發一套自動駕駛停車功能，也為後續自駕車子系統開發建立了驗證的平台。

This research integrates path planning algorithm and model predictive control, develops an autonomous driving parking system, and implement both algorithm in the car control computer Drive PX2 in the form of ROS package. The vehicle used in this research is CPEV, which developed by ITRI and China Motor. The vehicle model is built into the vehicle simulation software PreScan, and the communication between the Windows and the Linux operating system is established through the ROS Network. PreScan provides vehicle status and environmental data to the control strategy. After calculations, controller sends the control command back to PreScan, thus we establish a route between the two systems. In order to test the path feasibility of the path planning calculus and the accuracy of the model prediction controller trajectory tracking, this study uses the hardware-in-the-loop (HIL) for integration testing and verification, in order to increase the reality of the simulation, serially connected to the steering wheel, pedals and other hardware devices, and designed a hand-autonomous driving mode switching system, allowing the driver to freely switch the driving mode, and design the user interface, display the parking lot map in the RVIZ, allowing the driver to choose which parking space to park. In the obstacle avoidance strategy, through the RADAR sensor, emergency stops are used when encountering obstacles, the map is updated and the route is newly planned. In this study, the upper-level control and the lower-level control of the automatic driving are implemented. The experiments in three different scenarios are forward parking, reverse parking, and obstacle avoidance parking. In order to make the system more in line with the actual driving results, the simulated situation is also based on real field to build. This study completed the software and hardware integration of the automatic driving simulation, and developed an automatic driving parking function, and also established a verification platform for the subsequent self-driving subsystem development.

委員會審定 i
誌謝 ii
中文摘要 iii
Abstract iv
目錄 v
圖目錄 vii
表目錄 xi
符號表 xii
1 第一章、緒論 1
1.1 研究動機與目的 1
1.2 文獻回顧 4
1.3 本文貢獻 6
1.4 論文章節摘要 7
2 第二章、系統架構與系統模型 9
2.1 車控電腦PX2 9
2.2 PreScan 10
2.3 ROS 13
2.4 車輛模型 15
2.5 輪胎模型 27
3 第三章、路徑規劃演算法 34
3.1 動態規劃與路徑規劃演算法 34
3.2 戴科斯徹演算法 36
3.3 A*演算法 37
3.4 Hybrid A *演算法 40
4 第四章、模型預測控制 49
4.1 模型預測控制架構 50
4.2 線性時變模型預測控制算法 52
4.3 非線性系統線性化 57
4.4 目標函數與限制方程式 61
4.5 最佳化求解 63
4.6 穩定度分析 67
5 第五章、 HIL行車模擬驗證 73
5.1 實驗架構與硬體介紹 73
5.2 實驗通訊介紹 78
5.3 實驗流程 80
5.4 實驗結果與討論 87
6 第六章 結論與未來展望 123
6.1 結論 123
6.2 未來展望 123
參考文獻 126
附錄 130

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