臺灣博碩士論文加值系統

中文摘要
本文提出以模糊分佈分散式增廣型訊息濾波器（FDDEIF）及模糊分佈分散式增廣型卡爾曼濾波器（FDDEKF）等兩種定位方法，利用一組輪型四輪機器人、三輪機器人及四輪十字交叉型機器人所構成之異質多機器人系統，在彼此通訊不完全互相連接的情況下，藉由圖論理論及應用FDDEIF和FDDEKF來解決通信網路不完全連接的異質全向多機器人之全域姿態初始化及移動姿態估測問題進行全域合作定位。而其中合作式定位方法，是由其中一個安裝在異質機器人上的Kinect感測器及裝有雷射感測器的異質全向機器人偵測已知地標或QR碼，並以雷射感應器偵測周圍的環境，系統將使用姿勢初始化算法於估計該異質機器人的位置和方向，並將結果應用FDDEIF和FDDEKF方法傳遞於其他異質全向機器人，完成初始化異質全向行動機器人(HOMR)的全域姿態，然後融合一個雷射感測器和編碼器的測量結果，以實現HOMR的姿態追蹤。一旦得到其他異質全向機器人的初始方向與位置。則所有異質全向機器人的初始全域姿勢已大致確定，本文提出模糊分佈分散擴展訊息濾波器與模糊分佈分散式增廣型卡爾曼濾波器的動態合作定位方法，用以融合任意一台異質全向機器人上的Kinect感測器或雷射感測器與安裝在每個異質全向機器人馬達上的編碼器測量信息，使可連續地估測每一異質機器人的精確移動的姿勢。電腦模擬結果證明該多台異質機器人系統能夠有效的使用兩方法，在初始位置的情況下，利用各異質多機器人上的感測器合作定位，準確估測每一異質機器人的位置與方向角。

Abstract
This paper presents two cooperative global localization methods using fuzzy distributed and decentralized extended information filtering (FDDEIF) and fuzzy distributed and decentralized extended Kalman filtering (FDDEKF) for a heterogeneous omnidirectional multi-robots (HOMRs) system. This type HOMR system is composed of a car-like four-wheeled SWOR，a three-wheeled SWOR and a four-wheeled SWOR, where the communication topology is not completely connected to each other. When any known landmarks or QR codes are detect by a Kinect sensor in a heterogeneous robot, a pose initialization algorithm is proposed to estimate both start-up positions and orientations of that robot. Afterwards, a cooperative localization method is proposed to find initial poses of other heterogeneous robots that can be directly detected by a laser scanner mounted at the localized robot. Once the initial global poses of all heterogeneous robots have been roughly determined, both FDDEIF and FDDEKF cooperative localization methods are presented to fuse external measurements from one Kinect sensor, or one laser scanner and two encoders mounted on each heterogeneous robot, in order to keep track of the moving poses of all the heterogeneous robots continuously and precisely. Several simulations are conducted to show that the proposed methods are effective in finding accurate estimation of both unknown initial and continuous moving poses of a heterogeneous multi-robot system with three follower heterogeneous robots.

Contents
中文摘要……………. i
Abstract………. ii
Contents………….. iii
List of Figures vii
List of Tables xii
Nomenclature xiii
List of Acronyms xiv
Chapter 1 Introduction 1
1.1 Introduction 1
1.2 Literature Survey 4
1.2.1 Related Work on Localization for Mobile Robots 4
1.2.2 Related Work on Localization and Mapping for Heterogeneous Mobile Multi-Robots 5
1.3 Motivation and Objectives 6
1.4 Main Contributions 7
1.5 Thesis Organization 8
Chapter 2 Introduction Description of the Multi-HOMRs System 9
2.1 Introduction 9
2.2 HOMRs System Structure 10
2.2.1 Diagram of Multi-HOMR Formation System 10
2.2.2 Diagram of Single HOMR in Formation 11
2.3 ARM-based-controller (Type:STM32 NUCLEO-F446RE) 13
2.4 Raspberry-based Single board computer 14
2.5 Kinect sensor 16
2.5.1 The Prime Sensor 20
2.5.2 KINECT Calibration 21
2.5.3 KINECT -based Detection of Artificial Landmarks 22
2.6 Laser scanner 23
2.6.1 Laser ranger finder – RPLIDAR A2M8 23
2.6.2 Laser Scanner Detection of Walls 24
2.7 Encoders and Odometry 26
2.7.1 Encoders 26
2.7.2 Kinematic model 27
2.8 Experimental Results and Discussion 30
2.8.1 Experiment Results of the KINECT Landmark Detection 30
2.8.2 Experiment Results and Discussion on the Laser Scanner Detection …………………………………………………………………….33
2.9 Concluding Remarks 34
Chapter 3 Cooperative Global Localization Using Fuzzy Distributed and Decentralized EIF Algorithm for Multi-HOMR system 36
3.1 Introduction 36
3.2 Distributed and Decentralized EIF (DDEIF) Algorithm for Mobile Multirobots 38
3.2.1 Models of Omnidirectional mobile Multirobots 38
3.2.2 Modeling a Multi-HOMR Cooperative Localization System by a Modified Graph Theory 39
3.2.3 Distributed and Decentralized Extended Information Filtering Algorithm Using a Modified Graph Theory 39
3.3 Distributed and Decentralized Fuzzy EIF(FDDEIF) 42
3.3.1 Fuzzy Distributed and Decentralized EIF (FDDEIF) algorithm 43
3.3.2 Exponential Weighted DDEIF 44
3.4 Fuzzy Tuner 47
3.5 Map-Based Pose and Cooperative Pose Initialization 51
3.5.1 Map-Based Pose Initialization 51
3.5.2 Cooperative Pose Initialization 53
3.6 FDDEIF Global Dynamic Pose Tracking 55
3.7 Simulation and Discussion 57
3.7.1 Simulation 1: Cooperative Pose Initialization 57
3.7.2 Simulation 2: FDDEIF Dynamic Pose Tracking 58
3.8 Concluding Remarks 68
Chapter 4 Cooperative Global Localization Using Fuzzy Distributed and Decentralized EKF Algorithm for Multi-HOMR system Introduction 69
4.1 Introduction 69
4.2 Graph-Based Fuzzy DDEKF Algorithm 69
4.2.1 System Model of Each HOMR 70
4.2.2 Modeling a Multi-robot Cooperative Localization System by a Modified Graph Theory 70
4.3 Distributed and Decentralized Extended Kalman Filtering (DDEKF) Algorithm 71
4.4 FUZZY DDEKF (FDDEKF) Algorithm 75
4.5 Map-Based Cooperative Pose Initialization 78
4.5.1 Map-Based Pose Initialization 78
4.5.2 Cooperative Pose Initialization 80
4.6 FDDEKF Kinematic Pose Estimation 82
4.7 Simulations and Discussion 84
4.7.1 Simulation 1: Cooperative Pose Initialization 85
4.7.2 Simulation 2: FDDEKF Dynamic Pose Tracking 86
4.8 Concluding Remarks 94
Chapter 5 Conclusions and Future Work 95
5.1 Conclusions 95
5.2 Future Work 96
References ……………………………………………………………..98

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