Reliable indoor navigation of mobile robots has been a popular research topic in recent years. GPS systems used for outdoor mobile robot navigation can not be used indoor (warehouse, hospital or other buildings) because it requires an unobstructed view of the sky. Therefore a specially designed indoor localization system for mobile robot is needed. This project aims to develop a reliable position and heading angle estimator for real time indoor localization of mobile robots. Two different techniques have been developed and each consisted of three different sensor modules based on infrared sensing, calibrated odometry and calibrated gyroscope. Integration of these three sensor modules is achieved by applying the real time Kalman filter which provides filtered and reliable information of a mobile robot's current location and orientation relative to its environment. Extensive experimental results are provided to demonstrate its improvement over conventional methods like dead reckoning. In addition, a control strategy is developed to control the mobile robot to move along the planned trajectory. The techniques developed in this project have potentials for the application for mobile robots in medical service, health care, surveillances, search and rescue in indoor environments

Zhou, Jason

English

Massey Research Online

Copyright is owned by the Author of the thesis. Pennission is given for a copy to be downloaded by an individual for the purpose of research and private study only. The thesis may not be reproduced elsewhere without the pennission of the Author. Indoor Localization of a Mobile Robot Using Sensor Fusion A thesis presented in partial fulfillment of the requirements for the degree of Master of engineering . Ill Mechatronics with Honours at Massey University, Wellington, New Zealand. Jason Zhou August 2011 Supervisor: Dr. Loulin Huang Abstract Reliable indoor navigation of mobile robots has been a popular research topic in recent years. GPS systems used for outdoor mobile robot navigation can not be used indoor (warehouse, hospital or other buildings) because it requires an unobstructed view of the sky. Therefore a specially designed indoor localization system for mobile robot is needed. This project aims to develop a reliable position and heading angle estimator for real time indoor localization of mobile robots. Two different techniques have been developed and each consisted of three different sensor modules based on infrared sensing, calibrated odometry and calibrated gyroscope. Integration of these three sensor modules is achieved by applying the real time Kalman filter which provides filtered and reliable information of a mobile robot's current location and orientation relative to its environment. Extensive experimental results are provided to demonstrate its improvement over conventional methods like dead reckoning. In addition, a control strategy is developed to control the mobile robot to move along the planned trajectory. The techniques developed in this project have potentials for the application for mobile robots in medical service, health care, surveillances, search and rescue in indoor environments. 2 Acknowledgements I would like to acknowledge my Supervisor Dr. Loulin Huang, for his consistent support throughout the duration ofmy master degree. Thanks are also given to those people from the University, who has helped me a lot during the development process of this project. I would also like to express my deep gratitude to my family, especially to my parents for their constant encouragement, their tolerance and for their assistance in many ways for the successful completion of this thesis. Last but not least, I would like to thank those who always stood by me throughout my life and for their sacrifice and tolerance during the writing of this thesis. 3 Table of Content Abstract ................................................................................................................................................ 2 Acknowledgements .............................................................................................................................. 3 1 Introduction ....................................................................................................................................... 9 1.1 Aims ........................................................................................................................................... 9 1.2 Indoor localization of mobile robots .......................................................................................... 9 1.3 Overview of the developed indoor localization system ........................................................... 11 1.4 Structure of the thesis ............................................................................................................... 12 2 Literature reviews ........................................................................................................................... 13 2.1 Localization of mobile robots .................................................................................................. 13 2.1.1 Problems of localization .................................................................................................... 13 2.1.2 Localization methods ........................................................................................................ 15 2.1.2.1 Relative positioning method: ..................................................................................... 15 Odometry ................................................................................................................... 15 Calibration methods to improve Odometry ............................................................... 19 2.1.2.2 Absolute positioning methods: ................................................................................... 20 Landmark and beacon based navigation system ........................................................ 21 Northstar Localization system ................................................................................... 23 2.1.3 Calibration methods to improve speed estimation from encoder readings ....................... 27 3 The robot model and software development process ...................................................................... 29 3.1 Background on the robot model.. ............................................................................................. 29 3 .2 Background on robot control flow chart design ....................................................................... 32 3.3 The developed GUI for robot control ...................................................................................... 35 4 Proposed approaches and results ..................................................................................................... 3 8 4 .1 The development process ......................................................................................................... 3 8 4.2 Calibration methods used to improve odometry ...................................................................... 39 4.3 Calibration method to improve Gyroscope readings .............................................................. .40 4.4 Propose approach to improve speed estimation from encoder readings ................................. .42 4.5 Experiment to improve speed estimation from encoder readings ........................................... .44 4.5 .1 Results obtained at low speed using the proposed method .............................................. .45 4.5.2 Results obtained at high speed using the proposed method ............................................. .46 4.6 Kalman Filter ........................................................................................................................... 48 4.6.1 Implementing the Kalman Filter algorithm ..................................................................... .49 4 4. 7 Propose techniques for localization ......................................................................................... 52 4.7.1 Propose method one .......................................................................................................... 54 4.7.2 Propose method two .......................................................................................................... 55 4.8 Autonomous navigation implementation: ................................................................................ 56 4.9 Experiments and Results on developed techniques for localization ........................................ 59 4.9.1 Experiment one ................................................................................................................. 59 4.9.2 Experiment two ................................................................................................................. 64 4.9.3 Experiment Results ........................................................................................................... 65 5. Conclusions and future work ......................................................................................................... 67 6. References ...................................................................................................................................... 68 5 List of Figures Figure 1: A mobile robot navigating in an indoor environment ....................................................... 10 Figure 2: Overall structure of method one used for localization ....................................................... 11 Figure 3: Overall structure of method two used for localization ....................................................... 12 Figure 4: Body and navigation frame of a mobile robot.. .................................................................. 15 Figure 5: The P3DX mobile robot kinematics in the x -y plane ...................................................... 17 Figure 6: The Northstar localization system configured for navigation ............................................ 23 Figure 7: Detector Coordinates illustration ........................................................................................ 25 Figure 8: Room Coordinates illustration ............................................................................................ 25 Figure 9: Increasing the field of view by using multiple projectors .................................................. 26 Figure 10: The Pioneer3-DX mobile robot used in this project ......................................................... 29 Figure 11: Controlling the robot from the on-board computer via an RS-232 serial cable ............... 30 Figure 12: Controlling the robot remotely using the ArNetworking library ...................................... 31 Figure 13: The Client Server architecture for robot localization and navigation .............................. 32 Figure 14: The server program control flow chart for localization .................................................... 33 Figure 15: The client program control flow chart for localization .................................................... 34 Figure 16: The developed GUI for robot control.. ............................................................................. 35 Figure 17: The design process flowchart of the project ..................................................................... 38 Figure 18: The wheel layout diagram ................................................................................................ 39 Figure 19: The overall configuration of the speed improvement experiment.. ................................. .44 Figure 20: Speed estimation using the Classical method and the non-linear method at low speed .. .45 Figure 21: Speed estimation using the Classical method and the linear method at low speed .......... 45 Figure 22: Speed estimation using the Classical method and the exponential method at low speed 45 Figure 23: Speed estimation using the Classical method and the non-linear method at high speed .46 Figure 24: Speed estimation using the Classical method and the linear method at high speed ........ .46 Figure 25: Speed estimation using the Classical method and the exponential method at high speed46 Figure 26: The direct Kalman filter algorithm .................................................................................. .48 Figure 27: Integration of the calibrated odometry method and the calibrated gyroscope ................ .49 Figure 28: Testing the completed localization system with the mobile robot ................................... 52 Figure 29: Overall structure of proposed method 1 used for localization ......................................... 54 Figure 30: Overall structure of proposed method 2 used for localization ......................................... 55 Figure 31: Shows the body and navigation frame of the mobile robot .............................................. 56 Figure 32: The grid of 0.5 meter square that was placed one the floor ............................................. 59 6 Figure 33: Testing method for taking pose measurements ................................................................ 60 Figure 34: The N-point average window provided by Northstar ....................................................... 60 Figure 35: Different methods measured y-displacement vs true y-displacement.. ............................ 61 Figure 36: Proposed methods measured y-displacement vs true y-displacement.. ............................ 62 Figure 37: Different methods measured x-displacement vs true x-displacement. ............................. 62 Figure 38: Proposed methods measured x-displacement vs true x-displacement.. ............................ 62 Figure 39: Different methods measured hypotenuse displacement vs true hypotenuse displacement ............................................................................................................................................................ 63 Figure 40: Proposed methods measured hypotenuse displacement vs true hypotenuse displacement ............................................................................................................................................................ 63 Figure 41: Testing procedure for experiment two ............................................................................. 64 7 List of Tables Table 1: The control parameters used on the proposed method ....................................................... .45 Table 2: Summary of the relative speed error percentage for the methods used .............................. .4 7 Table 3: Summary table for error comparison of five different methods used for localization ........ 65 8 1 Introduction This section introduces the aims of this project and why indoor localization of mobile robots is important. And a brief overview of the developed indoor localization method for mobile robots is presented. Also the structure of the entire thesis is explained. 1.1 Aims The main research aim is to develop a reliable indoor localization system for mobile robots. This research was also done to achieve an understanding of the specific topic that is covered in this project. The secondary aim of the research was to identify new research topics for continuations of research study. 1.2 Indoor localization of mobile robots For a long time, accurate and reliable indoor localization of mobile robots have been a challenging research topic. This is due to common localization methods such as odometry which based on encoder readings have problems with accumulated errors. Other modem localization systems such as GPS (Global Positioning System) have several limitations. Most significantly, direct line of sight is required between the receiver and the satellites. Any objects obstructing this path can block the signal from a satellite making GPS suitable only for outdoor environment purposes. Therefore severely limiting its applications with mobile robots as a large proportion are designed for indoor use. 9 Figure 1: A mobile robot navigating in an indoor environment Many different methods have been developed in an attempt to solve the problems of robot localization [1 - 18] . These can be classified into the following two main categories: Relative Localization: The robot's position and orientation are determined relative to objects that are either stationary or moving in the environment. Pose coordinates are evaluated using data information provided by different on-board sensors, such as encoders, gyroscopes and accelerometers. Absolute Localization: The robot's absolute position and orientation are evaluated using data information provided by external sensors such as visual landmarks, navigation beacons or GPS. However, such technique required expensive installation of sensors, high maintenance and computational costs. Although the techniques described above can achieve localization for mobile robots, however they still face further physical limitations specific to the indoor environment. One of the ways to overcome this is to combine multiple sensory data from different sensor modules to provide better, reliable and accurate information of the robot's current location and orientation relative to its environment. Thus, in most mobile robot applications the relative and absolute positioning estimation methods have been employed together as one system to improve the localization performance [1 - 15]. 10 1.3 Overview of the developed indoor localization system In this project, a new solution for reliable indoor localization of mobile robots has been developed. The proposed localization system combines the absolute and relative position measurement obtained from three different sensor modules based on infrared sensing, calibrated odometry and calibrated gyroscope. Integration of the three sensor modules is achieved by implementing the Kalman filter technique in conjunction with a conditional algorithm created to analyze and to provide position measurement and heading angle data collected from the three different sensor modules. The Kalman filter technique is selected due to its ease of implementation and its ability to update multiple pose data continuously. Throughout the development process, two different approaches in our proposed method for localization have been developed and these can be represented by Figure 2 and Figure 3 respectively. Calibrated Calibrated Gyroscope {X, Y, theta), i {theta) (X, Y, theta) IKaim11nl1lter1-.-.>----~ tllH§?p&§~..J ' Conditional ' (X, Y, theta) Pose measurements used for localization Figure 2: Overall structure of method one used for localization 11 Calibrated. Gyroscope (X, Y, th (X, Y, theta) :···. ., ... I (theta) i ~----'Kiilr;i,an J:il~e_r~ t~it'nr,•j~n;.w! -'f I Conditional I ' Filtered ;heading anf)l!c! (X, Y, theta) Pose measurements used for localization Figure 3: Overall structure of method two used for localization Figure 2 and 3 shows the overall structure of the two different approaches of the developed indoor localization system. Essentially, each approach consists of the relative position measurement from the calibrated odometry and calibrated gyroscope and absolute position measurement from the optical beacon device known as the Northstar system [21]. The pose measurement collected from the three sensors modules are used to provide the information of the mobile robot's current location and orientation relative to its environment. The effectiveness of the two presented techniques and full description of the developed indoor localization system are explained in details in section 4. 1.4 Structure of the thesis Section 2 presents an introduction to the field of mobile robot localization and the fundamental problems of localization. Also different localization techniques and calibration methods used to improve localization performance will be discussed and explained in details. Section 3 presents the background of the robot model and details the software development process for robot control. Section 4 presents testing and calibrations methods used and experiments results are explained in details. In section 5, the conclusion of the thesis is presented and further development of the localization system and related work is discussed. 12 

Indoor localization of a mobile robot using sensor fusion : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Mechatronics with Honours at Massey University, Wellington, New Zealand

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Indoor localization of a mobile robot using sensor fusion : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Mechatronics with Honours at Massey University, Wellington, New Zealand

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