基於深度學習之人形機器人的影像辨識

Visual Recognition for Humanoid Robot Based on Deep Learning

蔡承恩(Cheng-en Tsai)

指導教授 ： 李祖添

共同指導教授 ： 劉智誠(Chih-Cheng Liu)

淡江大學/工學院/電機工程學系機器人工程碩士班/碩士(2019年)

https://doi.org/10.6846/TKU.2019.00564

摘要

本論文針對視覺自主之小型人形機器人提出一個基於深度學習(Deep Learning, DL)之影像辨識的實現方法，在Linux環境下以ROS建構人形機器人的軟體開發架構。在物體檢測與辨識中，將機器人所要辨識的目標先拍攝一定數量的樣本，透過數據增強(Data Augmentation)的方式來擴建資料包的照片數量，再以人工標記的方式標記訓練用的特徵區域，透過深度學習網路裡去學習特徵辨識，並利用單發多盒探測器(Single Shot Multibox Detector, SSD)標記出影像畫面中所要辨識的目標物，以此作為策略判斷的依據。最後，本論文使用NVIDIA出產的深度學習平台- Jetson TX2來執行深度學習的程式。由實驗結果可得知，本論文所設計之小型人形機器人的影像辨識系統，可使機器人在複雜環境中順利辨識出目標物，且成功執行策略並達到預期目標

關鍵字

卷積神經網路 ； 深度學習 ； 人形機器人 ； 機器人作業系統

並列摘要

In this thesis, a deep learning image recognition is proposed to be implemented on Robot Operating System (ROS) for a vision-based autonomous small-sized humanoid robot. In the Linux environment, ROS is used to establish the software development framework for the humanoid robot system. At object detection and identification, first captures a certain number of samples for the target to be identified robot. Second expands the number of photos in the data package through data augmentation, marks the feature areas for training by means of manual marking, and then use deep learning network to learn feature recognition. Through Single Shot MultiBox Detector to mark out the objects to be identified in the image. Finally use NVIDIA Jetson TX2 board to run the program. From the experimental results, we can see that the image recognition of the small-sized humanoid robot can make the robot successfully identify the target in complex environments, and successfully reach the desired goal.

並列關鍵字

Convolutional Neural Networks (CNN) ； Deep Learning (DL) ； Humanoid Robot ； Robot Operating System (ROS)

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