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標題: | 機器人的人類群體行為與人類移動認知地圖建構 Construction of Behavior Cognitive Map with Group Activities and Human Movement for Robots |
作者: | Cheng-Yun Cheng 鄭承昀 |
指導教授: | 黃漢邦(Han-Pang Huang) |
關鍵字: | 環境辨識模型,認知地圖,群體行為辨識,服務型機器人, Environment recognition,cognitive map,group activity recognition,service robot, |
出版年 : | 2020 |
學位: | 碩士 |
摘要: | 隨著科學及技術的進步,機器人的發展與應用已越來越廣泛。為了使機器人能更好地融入到我們的日常生活中,機器人必須理解環境並對抽象的規範有所認知。透過建立環境模型來瞭解人類行為與空間的關係是機器人認知中相當重要的一部分。其中,人與人的互動行為與人類的移動行為與環境的關係十分的密切。本論文致力於建構群體行為與人類移動的環境模型。針對群體行為的環境模型,本論文提出一個透過向量來描述人與人之間的關係的深度學習網路來進行群體行為的辨識並採用一個可以動態修正地圖的數學模型來適應環境中的變化。針對人類移動行為的環境模型,我們使用馬可夫鍊來描述人的各個位置上移動的意圖。藉由這兩個環境模型,機器人能理解各個位置適合做哪些行為,並對做出禁止行為的人給予適當的反應。 With the progress of science and technology, the development and application of ro-bots has become more extensive. For sharing the same environment and cooperating with human, robots need to understand the external environment and the abstract social rules of human society. Building the environment model to understand the relation between hu-man behavior and the environment is extremely important in robot perception. Human–human interaction behavior and human movement behavior are closely related to the en-vironment. As a result, this thesis is devoted to constructing an environment model com-posed of human–human interaction information and human movement information. For the model of human–human interaction, we propose a deep learning network, which uses a vector to describe the relation between two people, to recognize group activities and a mathematic model, which can dynamically adjust the parameters to adapt to the change in the environment. For the model of human movement, we use a Markov chain to describe the tendency of movement. With these two environment models, the robot can determine whether its behavior is appropriate or whether its behavior is prohibited at the location. Thus, the robot selects an appropriate strategy to interact with people. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/19865 |
DOI: | 10.6342/NTU202003428 |
全文授權: | 未授權 |
顯示於系所單位: | 機械工程學系 |
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U0001-1408202015213900.pdf 目前未授權公開取用 | 3.88 MB | Adobe PDF |
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