自走型物流搬運車之路徑規劃設計與分析

Design and Analysis in Path-planning of Autonomous Cargo Moving Carts

蘇兆林(Zhao-Lin Su)

指導教授 ： 王培仁

國立清華大學/工學院/動力機械工程學系/碩士(2020年)

摘要

隨著當今資訊及電腦科技的發展與進步，越來越多具自走操作的無人搬運系統出現於日常工作環境。自走無人化是近幾年快速發展的新興技術，根據資科領域的即時定位及地圖建構理論，並結合計算視覺系統輔助辨別能力，可達成自走導航及意外處理的功能。本論文針對文獻中已發表之即時區域地圖技術進行基本研究，對適用於無人物流自走搬運車之法則進行篩選，再結合計算視覺進行辨別路徑障礙物或行人，完成自走路徑規劃。對於地圖共享與群體互用計算資源，以去中心化架構完成群體間信息交流及分享，建構自走定位、路徑規劃、導航及群體信息交流的自走物流搬運車系統。
本論文首先進行即時區域地圖建構技術文獻回顧，其中包括使用傳統傳感器的即時區域地圖建構技術與使用視覺法的即時區域地圖建構技術，並對其基本定義與習知技術內涵，選擇其中合適方法測試於本研究之應用。進而針對文獻上習知之計算機視覺中的特徵點法與當下熱門的深度學習辨識法進行分析比較，選擇快速且準確之計算法則。最終使用機器人操作系統整合上述應用於自走搬運車之軟硬體系統，在此基礎上加入去中心化之構想與初步方法並進行相關技術之驗證，期望能提供自走物流搬運車之研究發展重要參考。

關鍵字

即時定位與區域地圖建構 ； 計算視覺 ； 去中心化技術

並列摘要

With the development and progress of information and computer technology, more and more unmanned handling systems with self-propelled operation appear in daily working environment. According to the theory of real-time positioning and map construction in the field of information science, combined with the auxiliary discrimination ability of computational vision system, it can achieve the functions of self-propelled navigation and accident handling. In this paper, based on the published real-time area map technology, the rules suitable for unmanned logistics self-propelled vehicles are screened, and then combined with computational vision to identify obstacles or pedestrians, the self-propelled path planning is completed. For map sharing and group interactive computing resources, information exchange and sharing among groups are completed by decentralized architecture, and a self-propelled logistics carrier system with self-propelled positioning, path planning, navigation and group information exchange is constructed.
Firstly, this paper reviews the literature of real-time regional map construction technology, including the real-time regional map construction technology using traditional sensors and the real-time regional map construction technology using visual method. Then, the basic definition and technical connotation of the real-time regional map construction technology are studied, and the appropriate methods are selected to test the application of this study. We compare the feature point method of computer vision in literature with the popular deep learning identification method, and choose the fast and accurate calculation method. Finally, the robot operating system is used to integrate the above software and hardware systems applied to self-propelled transporters. On this basis, the concept and preliminary method of decentralization are added, and the related technologies are verified. It is expected that this paper can provide an important reference for the research and development of self-propelled logistics carriers.

並列關鍵字

SLAM ； computer vision ； decentralization

參考文獻

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