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研究生:鄭嶧
研究生(外文):Jerry Cheng
論文名稱:基於多目標蟻群優化之模糊系統應用於自動停車
論文名稱(外文):Multi-Objective Ant-Colony-Optimization Based Fuzzy System for Mobile Car Automatic Parking
指導教授:吳俊德吳俊德引用關係
指導教授(外文):Gin-Der Wu
口試委員:洪志偉莊家峰
口試委員(外文):Jeih-weih HungChia-Feng Juang
口試日期:2023-06-28
學位類別:碩士
校院名稱:國立暨南國際大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2023
畢業學年度:111
語文別:英文
論文頁數:63
中文關鍵詞:模糊系統移動機器人自動停車多目標螞蟻群優化算法
外文關鍵詞:Ant Colony OptimizationFuzzy SystemAutomatic ParkingMobile robot
DOI:10.6837/ncnu202300129
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本論文提出了一種基於多目標群體優化的模糊系統方法,用於移動機器人的路邊平行停車。該方法利用一個模糊控制器根據四個雷達傳感器的測量和機器人行進方向與目標方向之間的角度,來控制機器人兩輪的速度。控制目標包括在未知環境中引導機器人接近停止目標、保持與牆壁的恒定距離、實現高速行進以及在停止時機器人達到目標方向。

為了優化模糊控制器的模糊規則,本研究使用了多目標螞蟻群體優化算法(MOACO)。通過使用Pareto非優解集和擁擠距離來評估模糊控制器的多目標函數值。實驗結果表明,在相同的疊代次數、群體規模和測試地圖條件下,所提出的方法在穩定性方面優於粒子群優化(PSO)算法,並且最終的停止結果更接近目標位置。

This paper proposes a fuzzy system based on multi-objective ant-colony-optimisation for automatic parking applications to control the stopping behaviour of a mobile robot. The approach employs a fuzzy controller that adjusts the robot's wheel speed based on radar sensor measurements and the angle between the robot's direction and the target direction. The objectives include guiding the robot towards the stopping target, maintaining a constant distance from the wall, achieving high-speed travel, and reaching the target direction when the robot is stopped.

A Multi-Objective Ant Colony Optimization Algorithm (MOACO) is utilized to optimize the fuzzy controller's rules. Comparative experiments with the Particle Swarm Optimization (PSO) algorithm demonstrate the superior stability and closer proximity to the target achieved by the proposed method. The findings highlight the effectiveness of the multi-objective swarm optimization approach for mobile robot stopping control.

Acknowledgements i
摘要 ii
Abstract iii
Table of Contents iv
List of Tables vi
List of Figures vii
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Related Application 2
1.3 Related Research 5
1.4 Purpose 7
1.5 Thesis Architecture 8
Chapter 2 Fuzzy Control Introduction 9
2.1 Origin of Fuzzy Control Theory 9
2.2 Fuzzy System Architecture 11
2.3 Practical Implementation Architecture 14
2.3.1 Layer 1 16
2.3.2 Layer 2 17
2.3.3 Layer 3 18
2.3.4 Layer 4 20
2.4 Fuzzy Controller Optimization Techniques 22
Chapter 3 Multi-Objective Ant Colony Optimization 24
3.1 MOACO origin 25
3.1.1 ACO 25
3.1.2 Multi-Objective Optimisation Algorithms 27
3.1.3 The Framework of the Multi-Objective Ant Colony Optimization algorithm. 29
3.2 Multi-Objective Algorithms 32
3.2.1 Multi-Objective Function Evaluation 32
3.2.2 Nondominated Sorting 35
3.2.3 Crowding Distance Sorting 37
3.3 The Method of New Solution Generation 40
3.3.1 Pheromone Level-Based Elite-Tournament Path Selection Operation 40
3.3.2 Gaussian Sampling 42
3.3.3 Front-Guided Moving Operation 43
Chapter 4 MOACO-Based Automatic Parking System Simulation Results 44
4.1 Simulation Setup 44
4.1.1 Introduction to The Simulation Platform 44
4.1.2 Simulation Map 47
4.1.3 Mobile Robot Description 48
4.2 Simulation Results 50
Chapter 5 Discussion and Conclusion 55
5.1 Research Contribution 55
5.2 Following with Obstacles 57
5.3 Future Work and Outlook 59
References 61


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