基於機器人作業系統設計之跨樓層自主式導航服務型機器人

Autonomous Cross-Floor Navigation System for a ROS-Based Modular Service Robot

王文新(Wang Wen-Hsin)

指導教授 ： 許陳鑑 ； 王偉彥

國立臺灣師範大學/科技與工程學院/電機工程學系/碩士(2019年)

https://doi.org/10.6345/NTNU201900970

摘要

本論文實現一基於機器人作業系統(Robot Operating System)之跨樓層自主式導航服務型機器人，使機器人能夠於多樓層之間進行導航任務，其功能包括建置地圖、定位、路徑規劃、以及樓層辨識。建置地圖方面使用了Gmapping，配合雷射測距儀可以建置所需的二維平面地圖。定位使用了「自適應蒙地卡羅定位法」(Adaptive Monte Carlo Localization, AMCL)，配合雷射測距儀所接收到的環境資訊，可以計算出機器人在地圖上的所在位置，同時使用所得到的定位結果，讓機器人達到良好的行走效果。路徑規劃使用了「改良型A*路徑規劃」，讓機器人規劃出一條避開地圖上障礙物的路徑，以安全到達目的地。由於需要在多樓層之間進行導航任務，所以本文也提出了一個以深度卷積神經網路設計的決策系統，用於辨識樓層及讀取該樓層的地圖資訊，透過訓練大量的樓層場景圖片資料，得到的訓練模型能夠辨識出當前樓層。為了驗證此導航系統之效能及可行性，本論文中在擁有多樓層的室內環境中進行實驗，由實驗結果得知，本論文提出之導航以及樓層辨識系統能於多樓層之間有效地進行導航任務。

關鍵字

跨樓層導航系統 ； 服務型機器人 ； 機器人作業系統 ； 深度卷積網路

並列摘要

In this thesis, an autonomous cross-floor robot is implemented based on robot operating system (ROS) with functionalities including path planning, mapping, localization, and scene recognition. Various navigation techniques are used in this thesis, where 2D maps of the environment that the robot needs are built by the Gmapping algorithm, Adaptive Monte Carlo Localization algorithm is utilized in localization, and an improved A* algorithm is utilized to do path planning avoid obstacles on the path. Since the robot needs to perform navigation task in a multi-floor environment, a decision system based on deep convolution neural network is designed to recognize the floors by training with a lot of scene images. Finally, to validate the feasibility of the proposed method, real-world experiments of this proposed system are conducted.

並列關鍵字

Cross-floor navigation system ； Service robots ； Robot operating system (ROS) ； Deep convolutional neural network (DCNN)

參考文獻

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