發展具高速計算能力之智慧機器人系統

Development of intelligent robotic systems with high-performance computing capability

陳文東(Wen-Tung Chen)

指導教授 ： 王銀添

淡江大學/工學院/機械與機電工程學系碩士班/碩士(2020年)

https://doi.org/10.6846/TKU.2020.00649

摘要

因應智慧機器人的高速計算能力的需求，系統建置可分為雲端計算與邊緣計算兩種形式。雲端計算因為無法即時更新資訊，不適合於機器人平台。而邊緣計算能達到體積遠小於個人電腦並擁有相同計算能力的系統運作，達到即時運算之要求。本研究探討在嵌入式平台上結合深度學習演算法的應用，以開發具高速計算能力的機器人系統。研究議題包括分析深度學習模型、架構機器人作業系統、蒐集與處理相機影像資料等。將處理的影像訊息傳送至深度學習模型進行推論，結果再使用機器人作業系統中的元件進行機器人的運動控制。測試的系統使用Robotis公司Turtlebot3與輝達(Nvidia)公司的Jetson Terga X2運算模組進行整合，程式使用C++與Python等語言撰寫。最後使用整合的系統驗證所提出演算法的實用性。

關鍵字

深度學習推論 ； 機器人作業系統 ； 邊緣運算

並列摘要

In response to the requirement of high computing capability for intelligent mobile robot systems, two different approaches were considered including cloud computing and edge computing. Because the cloud computing might not able to update information in real time, it is not suitable for the mobile robot system. On the other hand, the edge computing built in the mobile robot can provide real-time information for system. This study presents an embedded edge computing system based on the deep learning algorithm to ensure the high computing capability. Research topics include deep learning models, robotic operating systems (ROS), and visual sensing and image processing. The processed image information was sent to the deep learning model for command inference. The results were used as the ROS function to control the robot motion. An Nvidia Jetson Terga X2 embedded board was used as an edge computing system for a Robotis mobile robot - Turtlebot3. The deep learning inference mechanism was developed using C++ and Python programming language. The hardware and software of the intelligent mobile robot system were integrated to validate the practicality of the proposed algorithms.

並列關鍵字

Deep Learning inference ； Robot Operating System ； Edge Computing

參考文獻

參考文獻

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