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系統識別號 U0026-2307201815075200
論文名稱(中文) 自走車最佳化軌跡規劃、軌跡追蹤和障礙物閃避
論文名稱(英文) Optimization-based Path Planning, Trajectory Tracking and Obstacle Avoidance of 1:10 scale RC car
校院名稱 成功大學
系所名稱(中) 航空太空工程學系
系所名稱(英) Department of Aeronautics & Astronautics
學年度 106
學期 2
出版年 107
研究生(中文) 何冠廷
研究生(英文) Guan-Ting He
學號 P46051270
學位類別 碩士
語文別 英文
論文頁數 53頁
口試委員 指導教授-譚俊豪
口試委員-詹劭勳
口試委員-彭兆仲
中文關鍵字 自動駕駛  最佳化控制  模型預測控制  軌跡規劃  障礙物閃避  自動停車  Frenet Planning 
英文關鍵字 Autonomous Vehicle  Optimal Control  MPC  Trajectory Tracking  Obstacle Avoidance  Frenet Planning  Path Planning  Auto Parking 
學科別分類
中文摘要 在未來自動駕駛的車輛能感測環境及導航,在遇到突發狀況能做出快速且即時的判斷。本篇著重於車輛路線追蹤且偵測到障礙物能適時的做出閃避或是停下避免碰撞。主要架構是基於數學上的最佳化方式為核心,經由車輛動態模型預測控制的方法去達成目標。
車輛動態模型預測控制做路徑規劃及追蹤,使用車輛動態模型,制定成標準的最佳化問題,再藉由數值解的程式得到最佳解;障礙物閃避的部分則是系統根據當下的位置及車輛動態,並把雷射偵測到的障礙物設成有效限制放進最佳化問題裡,進一步完成閃避。本篇完成了軌跡追蹤、障礙物閃避及自動停車的模擬。
在實驗中,利用模型預測控制做障礙物閃避相對於軌跡追蹤,需要花較長的計算時間,所以沒辦法在這篇論文順利完成實驗。在這提出另一個路線規劃的演算法Frenet Planning,規劃一條沒有發生碰撞的路線,再使用模型預測控制追蹤路線完成避障實驗。
英文摘要 In the future, the autonomous vehicles will be able to sense the environment, navigate on their own and handle unexpected conditions. This thesis focuses on the trajectory tracking, obstacle avoidance and auto parking.
In the thesis, we use Model Predictive Control (MPC) for experiments. MPC utilizes the current states of the vehicle and the valid constraints to formulate an optimization problem, and then the car is able to safely dodge the objects to achieve the goal. In this thesis, we already completed the simulations of trajectory tracking, obstacle avoidance and auto parking.
In the experiment, we use Robot operating system (ROS) providing a useful platform for connecting different nodes to implement MPC. Besides, Julia is a programming language fusing ROS, other programming languages and numerical solvers such as IPopt to develop our experiment platform. However, using MPC to avoid obstacles require a long calculation time, it’s impossible to implement on the car. In the thesis, another trajectory planning algorithm Frene ́t Planning is applied to plan a collision-free path, we use model predictive control to track the path achieving obstacle avoidance in real time.
論文目次 Contents
中文摘要...............................................i
Abstract..............................................ii
致謝..................................................iii
List of Tables........................................vi
List of Figures.......................................vii
List of Symbols.......................................ix
List of Abbreviation..................................x
Chapter 1 Introduction................................1
1.1 Motivation....................................1
1.2 Goals.........................................2
Chapter 2 Autonomous Ground Vehicle system............3
2.1 Hardware......................................3
2.2 Software......................................6
Chapter 3 Dynamics Model..............................7
3.1 Dynamics of The Car...........................7
3.2 Non-Slip Model................................10
3.3 Equations of Motion...........................12
Chapter 4 MPC for Trajectory Tracking.................17
4.1 Model Predictive Control......................17
4.2 MPC for Trajectory Tracking...................18
4.3 Simulations...................................18
4.4 Experiments...................................22
Chapter 5 MPC for Obstacle Avoidance..................25
5.1 Detection Region..............................25
5.2 MPC for Obstacles Avoidance:..................26
5.3 Simulations...................................28
5.4 Discussions...................................32
Chapter 6 Frenet Path Planning for Obstacle Avoidance.33
6.1 Lateral Path Planning:........................34
6.2 Longitudinal Path Planning:...................36
6.3 Frene ́t Path Planning.........................36
6.4 Experiments...................................38
Chapter 7 Autonomous Parking..........................40
7.1 Two Layer MPC for Autonomous Parking..........40
7.2 Simulations...................................43
7.3 Experiments...................................48
Chapter 8 Conclusions.................................50
8.1 Summary.......................................50
8.2 Future Works..................................51
Bibliography..........................................52

參考文獻 [1]S. C. K. Ho, "Autonomous Trajectory Tracking and Obstacle Avoidance via Pure Pursuit and Optimization", 2017.
[2]"Massachusetts Institute of Technology (MIT) RACECAR Project.," [Online]. Available: https://mit-racecar.github.io/.
[3]"LOCOCAR Project Source Code.," [Online]. Available: https://github.com/mrsd16teamd/loco_car.
[4]Patrick Spengler & Christoph Gammeter, "Modeling of 1:43 scale race cars," 2010.
[5]A. Liniger, "Autonomous Drift Control," 2012.
[6]L. Wunderli, MPC based Trajectory Tracking for 1:43 scale Race car, 2011.
[7]Y. Gao, Model Predictive Control for Autonomous and Semiautonomous Vehicles, 2014.
[8]YongsoonYoon, JonghoShin,, "Model-predictiveactive steering and obstacle avoidance for autonomous ground vehicles," 2008.
[9]"BARC Project Source Code," [Online]. Available: https://github.com/MPC-Berkeley/barc.
[10]Iian Dunnling, Joey Huchette, Miles Lubin, JuMP: A Modeling Language for Mathematical Optimization, 2017.
[11]Moritz Werling, Julius Ziegler, S¨oren Kammel, and Sebastian Thrun, Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame, 5 2010.
[12]J. H. M. L. Iain Dunning, “JuMP: A Modeling Language for Mathematical Optimization,” 2017.
[13]Y. Kuwata, "Real-time Trajectory Design for Unmanned Aerial Vehicles using," 2003.
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