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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