Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/6910
DC FieldValueLanguage
dc.contributor蘇武昌zh_TW
dc.contributor宋開泰zh_TW
dc.contributor.advisor蔡清池zh_TW
dc.contributor.author王梓竹zh_TW
dc.contributor.authorWang, Tzu-Chuen_US
dc.contributor.other中興大學zh_TW
dc.date2012zh_TW
dc.date.accessioned2014-06-06T06:39:10Z-
dc.date.available2014-06-06T06:39:10Z-
dc.identifierU0005-2107201116120800zh_TW
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dc.identifier.urihttp://hdl.handle.net/11455/6910-
dc.description.abstract本論文的首要目的是對於兩輪自平衡自動服務機器人提出智慧型運動控制與路徑規劃法。首先,以未知參數之動態數學模型為基礎的前提之下,將該平台的未知函數分成已知與未知項,並提出模糊基底函數的智慧型運動控制策略來學習未知項的變動,進而改善其軌跡追蹤控制之性能。在規劃最佳路徑與防碰撞方面,本論文提出以修正型粒子群尋優演算法來進行規畫全域路徑規劃,並以彈性帶技術來規劃區域路徑。兩路徑規劃法可結合上述的運動控制法則,在機台運用上實際運行。最後本文利用電腦模擬與進行實際機台實驗,用以檢驗所提的運動控制法則及路徑規劃的可行性與效用性。實驗的循跡結果說明所提出的智慧型運動控制在軌跡追蹤控制上具有滿意的控制功能。zh_TW
dc.description.abstractThis thesis presents techniques for intelligent adaptive motion control and path planning of a two-wheeled self-balancing mobile robot. First, based on the dynamic mathematical model whose system functions can be decomposed into the nominal and perturbed terms, an intelligent motion controller augmented with fuzzy basis function networks is designed to achieve trajectory tracking control; this controller is composed of two control modules: posture and speed tracking, and yaw rate control. To find optimal and collision-free paths for the robot, this thesis also presents a global path planning method using the modified particle swarm optimization (MPSO) method, and then establishes a local path planning scheme using the elastic band technology. Several simulations are conducted to illustrate the feasibility and effectiveness of the proposed intelligent adaptive trajectory tracking method, PSO-based global path planning method and elastic-band-based local path planning scheme. Furthermore, some experimental results on for trajectory tracking are performed to show that the proposed intelligent motion controller is capable of giving satisfactory trajectory tracking control performance.en_US
dc.description.tableofcontents誌 謝 辭 i 中文摘要 ii Abstract iii Contents iv List of Figures viii List of Tables xiii Nomenclature xiv List of Acronyms xv Chapter 1 Introduction 1 1.1 Introduction 1 1.2 Literature Review 5 1.2.1. Related Work for Control of the SBTWMRs 5 1.2.2. Related Work for Global Path Planning 6 1.2.3. Related Work for Local Path Planning. 7 1.3 Motivation and Objectives 8 1.4 Main Contributions 8 1.5 Thesis Organization 9 Chapter 2 System Structure and Control Architecture 10 2.1 Introduction 10 2.2 Overall System Structure and Key Components 10 2.2.1. Motor Drive 14 2.2.2. Motor 15 2.2.3. Right angle Gearboxes 16 2.2.4. Power Supply Module 17 2.2.5. DC-AC Module 17 2.2.6. Wheels 18 2.2.7. Tilt Sensor 18 2.2.8. Gyro Sensor 20 2.2.9. Rotary Encoder 22 2.2.10. Battery Energy Monitor 24 2.2.11. Digital Signal Controller (DSC) 25 2.2.12. Signal Flow of the Overall Control System 30 2.2.13. Laser Sensor 30 2.3 Motion Control System 32 2.3.1. Digitization of the Torque-to-Speed Conversion 33 2.3.2. Dead-Reckoning 34 2.3.3. Sensing and Signal Processing 35 2.4 Concluding Remarks 36 Chapter 3 Intelligent Motion Control Using Fuzzy Basis Function Networks for Uncertain Self-Balancing Two-Wheeled Robots 37 3.1 Introduction 37 3.2 Uncertain Mathematical Model 37 3.3 Brief Review of FBFN 41 3.4 Problem Statement and Kinematics Controller Design 44 3.4.1. Problem Statement 44 3.4.2. Design of Trajectory Tracking Controller 44 3.5 Intelligent Adaptive Sliding-Mode Posture and Speed Control Controllers Design Using FBFN 45 3.6 Intelligent Adaptive Sliding-Mode Yaw Rate Control Controllers Design Using FBFN 50 3.7 Simulations and Discussion 53 3.8 Experimental Results and Discussion 65 3.9 Concluding Remarks 68 Chapter 4 Modified Particle Swarm Optimization Algorithm for Global Path Planning 69 4.1 Introduction 69 4.2 Grid-based Modified Particle Swarm Optimization (MPSO) Algorithm 70 4.2.1. Searching Block Construction 73 4.2.2. Particle Swarm Optimization (PSO) Blocked Area Search 82 4.2.3. Quadratic B-Spline 85 4.3 Simulation Result and Discussion 86 4.4 Experiment Result and Discussion 90 4.5 Concluding Remarks 92 Chapter 5 2D Collision-free Trajectory Generation by Elastic Band Technique for Local Path Planning 93 5.1 Introduction 93 5.2 Local Path Planning Using Elastic Band Technique 93 5.3 Real-time Trajectory Generation Using Elastic Band Technique 94 5.3.1. Initial Path Build-up 95 5.3.2. Elastic Band Deformation 96 5.3.3. Bubble Reorganization 97 5.3.4. Trajectory Transformation 98 5.3.5. Real-time Algorithm for an Elastic Band Trajectory Generation 99 5.4 Simulations and Discussion 100 5.4.1. Static Obstacle Avoidance 100 5.4.2. Dynamic Obstacle Avoidance 101 5.5 Concluding Remarks 103 Chapter 6 Conclusions and Future Work 104 6.1 Conclusions 104 6.2 Future Work 105 References 107zh_TW
dc.language.isoen_USzh_TW
dc.publisher電機工程學系所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2107201116120800en_US
dc.subject兩輪自平衡zh_TW
dc.subjectSBTWMRen_US
dc.subject倒單擺zh_TW
dc.subjectSelf-Balancing Two-Wheeled Mobile Roboten_US
dc.subjectwheeled inverted pendulumsen_US
dc.title兩輪自平衡行動機器人之智慧適應運動控制及路徑規劃zh_TW
dc.titleIntelligent Adaptive Motion Control and Path Planning for a Self-Balancing Two-Wheeled Mobile Roboten_US
dc.typeThesis and Dissertationzh_TW
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeThesis and Dissertation-
item.cerifentitytypePublications-
item.fulltextno fulltext-
item.languageiso639-1en_US-
item.grantfulltextnone-
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