臺灣博碩士論文加值系統

本論文旨在實作一差速輪機器人，並以粒子濾波器(Particle Filter, PF)演算法，結合里程計和雷射測距儀(Laser Range Finder, LRF)，控制所完成之機器人執行室內同時定位與建圖(Simultaneous Localization And Mapping, SLAM)任務。
本研究所製作之差速輪機器人以現場可程式化閘陣列(Field Programmable Gate Array, FPGA)為底層硬體平台，負責接收來自機器人作業系統(Robot Operating System, ROS)之脈衝寬度調變(Pulse Width Modulation, PWM)控制訊號，並驅動馬達；以及接收馬達編碼器之脈衝訊號，並傳送至機器人作業系統(Robot Operating System, ROS)，作為計算機器人位姿之里程計資訊。此外，本研究在底層以PID控制法則控制馬達之運轉，以確保馬達能準確達到控制命令所要求轉速。
模擬以及實驗結果顯示，本文所完成之差速輪機器人在室內環境移動，到達目標之位置誤差平均在0.15公尺內，角度誤差在10度內。

This article aims to implement a different-drive mobile robot that can perform indoor SLAM (Simultaneous Localization and Mapping) tasks. By collecting the sensory data from the odometry of the robot and a LRF (Laser Range Finder) mounted on the robot, we adopt the PF (Particle Filter) algorithm on the accomplished robot to successfully complete an indoor SLAM task.
In this research, the robot uses a FPGA (Field Programmable Gate Array) board to be its main circuit. The board is responsible for receiving the PWM (Pulse Width Modulation) control signal from the ROS (Operating Robot System) to drive the motors; and reading the pulses from Hall encoders of the motors and delivering them to the ROS through RS-232. The encoder data is used for calculating odometry of the robot. In addition, we used the PID control method to control the motors’ speed confirming that the robot can follow the control command from the ROS.
From the simulation and experimental results, the robot can reach the goal with location error less than 0.15 meters and orientation error less than 10 degrees.

摘要....................i
Abstract....................ii
誌謝....................iii
目錄....................iv
表目錄....................vi
圖目錄....................vii
第一章 緒論....................1
1.1 簡介....................1
1.2 文獻探討....................2
1.3 研究動機 ....................2
1.4 研究方法....................3
1.5 論文架構....................5
第二章 演算法....................6
2.1 Rao-Blackwellized Particle Filter( RBPF )演算法.................... 6
2.2 Dynamic Window Approach(DWA)演算法....................8
2.3 Dijkstra演算法....................9
第三章 即時定位、建圖與導航模擬....................11
3.1 Robot Operating System (ROS)[1]....................11
3.2 TransForm(TF)....................12
3.3 Gazebo....................13
3.4 Rviz....................13
3.5 URDF( Unifield Robot Description Format )....................13
3.6 模擬環境搭建....................14
3.7 差速輪機器人模型....................15
3.8 即時建圖與定位模擬....................16
3.9 導航模擬....................17
第四章 差速輪機器人平台系統架構介紹及整合....................20
4.1 差速輪移動平台機構....................20
4.2 硬體電路模組介紹....................21
4.3 FPGA開發環境介紹....................26
4.4 系統整合....................26
4.5 PID控制器....................30
4.6 同時定位與建圖實驗....................35
4.7導航及避障實驗....................38
第五章 結論與未來展望....................44
5.1 討論與結論....................44
5.2 本研究之貢獻....................44
5.3 未來展望....................44
參考文獻.................... 46
Extended Abstract....................48

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[20]TF，http://wiki.ros.org/tf
[21]TF轉換示例，https://www.ncnynl.com/archives/201708/1881.html
[22]Gazebo，http://wiki.ros.org/gazebo
[23]Rviz，http://wiki.ros.org/rviz
[24]URDF，http://wiki.ros.org/urdf/Tutorials
[25]Link與Joint連結之範例，https://reurl.cc/4m08ND
[26]Gmapping package，http://wiki.ros.org/gmapping
[27]Move_base package，http://wiki.ros.org/move_base
[28]直流無刷馬達，https://w.wiki/4DMm
[29]HOKUYO Laser Range Finder UST-20LX，https://hokuyo-usa.com/products/lidar-obstacle-detection/ust-20lx
[30]霍爾編碼器，https://w.wiki/EEG
[31]PID控制器，https://w.wiki/4HWu
[32]Static_Transform_Publisher，http://wiki.ros.org/tf#static_transform_publisher