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研究生:蕭信揚
研究生(外文):Hsin-YangHsiao
論文名稱:應用立體視覺與粒子群最佳化設計實現FIRA大型人形機器人之競賽策略
論文名稱(外文):Design and Implementation of Competition Strategies for FIRA Adult-sized Humanoid Robot by Using Stereo Vision System and Particle Swarm Optimization
指導教授:李祖聖
指導教授(外文):Tzuu-Hseng Li
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電機工程學系碩博士班
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:2012
畢業學年度:100
語文別:英文
論文頁數:118
中文關鍵詞:大型人形機器人立體視覺粒子群最佳化
外文關鍵詞:Adult-sized Humanoid RobotStereo VisionParticle Swarm Optimization
相關次數:
  • 被引用被引用:1
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  • 下載下載:5
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本論文旨在建立大型人形機器人立體視覺影像與控制策略系統,並實際應用於FIRA國際機器人競賽HuroCup大人形組;本大型人形機器人主要分成立體影像系統與控制策略兩部份,其影像處理與策略控制核心採用筆記型電腦為中央運算模組,並連接兩台網路攝影機做為視覺感知器。論文首先介紹本大型人形機器人之硬體系統架構與規格,接著是立體視覺系統的建立與策略系統的流程控制。在視覺系統方面,首要工作是雙眼攝影機的校正,使用區塊匹配演算法計算立體影像的視差得到深度資訊,重建周圍環境的三維座標系統,最後我們提出強健且快速的搜尋法以辨識不同種類的特徵物體,並獲得各物件的相對位置。在策略控制方面,對於較高技術性的避障競賽,提出基於粒子群最佳化的演算法,簡化策略判斷,尋找較短且適合機器人行走的路線。針對其餘著重精準度與穩定度的項目,以其特性區分成不同的階段,設計最適合的控制策略系統,以達成最佳的任務執行時間。最後經實驗與模擬結果,可充分展現本大型人形機器人在視覺與策略上優越的效能與強健性。
This thesis mainly concerns the development of the stereo vision and strategy control systems for the FIRA Adult-sized HuroCup competition. The entire system is regarded as a vision feedback control system. The overall processes of vision and decision-making are processed on a laptop. The stereo visual images are captured by two CMOS webcam sensors. Firstly, the thesis introduces the hardware architecture of adult-sized humanoid robot, aiRobots-AH-I, and overview of software system. In the stereo vision system, we calibrate and rectify stereo cameras, and use Block-Matching algorithm to correspond the images. Then, the three-dimensional coordinate system of the surrounding environment with coordinate transformation are reconstructed. We propose a recursive searching algorithm to recognize and segment the objects such as the line, landmark, obstacles, ball, and goal in the events. Furthermore, a simplified cam-shift concept is adopted to track the found objects by driving the head motors. In the control strategy system, five events, including marathon, sprint, basketball, obstacle run, and penalty kick, are examined and emphasized. For the highly skilled obstacle run contest, particle swarm optimization algorithm is utilized to find a shorter and suitably walkable path for the robot without touching the obstacles. For the other events, we divide the strategy into multi-stages according to the characteristics of each event and design the most appropriate decision-making system to have the best performing time. Finally, experimental results fully demonstrate the superior performance and robustness of vision and strategy systems in our adult-sized humanoid robot.
Chapter 1. Introduction 1
1.1 Motivation 1
1.2 Thesis Organization 4

Chapter2. Hardware and Software of the Adult-sized Humanoid Robot 5
2.1 Introduction 6
2.2 The Hardware Configurations and Specifications of aiRobots-AH-I 9
2.2.1 Actuators 9
2.2.2 Motion Controller 11
2.2.3 Computer 15
2.2.4 Camera 16
2.2.5 ZigBee Module 18
2.2.6 9-axes IMU 19
2.2.7 LCD Display 20
2.2.8 Li-poly Batteries 21
2.3 Overview of Vision and Strategy System 22
2.3.1 The Human Machine Interface of Vision System 24
2.3.2 The Human Machine Interface of Strategy System 26
2.4 Summary 28

Chapter 3. Stereo Vision System 29
3.1 Introduction 29
3.2 Camera Calibration 30
3.3 Stereo Calibration and Rectification 34
3.4 Three-Dimensional Reconstruction 41
3.4.1 Stereo Correspondence 41
3.4.2 Image Reprojection 47
3.4.3 Coordinate Transformation 48
3.5 Summary 51

Chapter 4. Object Recognition and Control Strategy 53
4.1 Introduction 53
4.2 Image Recognition and Object Tracking 54
4.2.1 Color Space Conversion and Speed Up 55
4.2.2 Recursive Searching Algorithm 57
4.2.3 Kalman Filter 62
4.2.4 Target tracking 63
4.3 Marathon 65
4.3.1 Object Recognition 65
4.3.2 Control Strategy 67
4.4 Sprint 69
4.4.1 Object Recognition 69
4.4.2 Control Strategy 71
4.5 Basketball 75
4.5.1 Object Recognition 76
4.5.2 Control Strategy 77
4.6 Obstacle Run 81
4.6.1 Object Recognition 81
4.6.2 Particle Swarm Optimization 82
4.6.3 Control Strategy 88
4.7 Penalty Kick 90
4.7.1 Object Recognition 91
4.7.2 Control Strategy 91
4.8 Summary 95

Chapter 5. Experiment Results 96
5.1 Introduction 96
5.2 Experimental Results of Strategy for Marathon Event 97
5.3 Experimental Results of Strategy for Sprint Event 99
5.4 Experimental Results of Strategy for Basketball Event 102
5.5 Experimental Results of Strategy for Obstacle Run Event 104
5.6 Experimental Results of Strategy for Penalty Kick Event 107

Chapter 6. Conclusions and Future Works 110
6.1 Conclusions 110
6.2 Future Works 113

References 114
Biography 118

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