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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 黃漢邦(Han-Pang Huang) | |
dc.contributor.author | Po-Wei Wu | en |
dc.contributor.author | 吳柏緯 | zh_TW |
dc.date.accessioned | 2021-06-12T18:18:13Z | - |
dc.date.available | 2013-08-10 | |
dc.date.copyright | 2011-08-10 | |
dc.date.issued | 2011 | |
dc.date.submitted | 2011-08-08 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/27741 | - |
dc.description.abstract | 隨著機器人學的蓬勃發展,機器人已經從過去的工業環境或生產線,走進人類的日常生活。不管是寵物型機器人、居家看護機器人或是導覽機器人,在可見的未來必定會逐漸頻繁出現在人類的環境中,像是學校、辦公室、醫院、美術館、甚至在家庭中。而為了要使機器人能夠更加適應人類所處的環境,也為了提高人類對於機器人的接受度,機器人必須要了解人類行為與相對應環境的關係。更精確地說,由於人類的行為高度地受到如文化風俗、法律、甚至是心理狀態等隱晦因素的影響,而機器人若想要被人類接納,就必須入境隨俗,嘗試理解人類高度社會化的空間利用行為,並且遵守共同的社交規範。
本論文的宗旨即在於發展「廣義空間行為認知模型」 ,這套模型教導機器人如何在各種環境中學習其特有的規則。除了就理論部分詳加說明之外,本論文也舉出數種常見的場景當作範例,演示機器人如何藉由模型所述之方法學習,同時也揭露機器人能夠藉由此種方式,無窮無盡地累積知識以供使用,最終達到在各種社交場合中均能夠表現合宜的目的。 而本論文也展示如何利用同樣的模型架構,來推測人類的喜好。一旦機器人能夠掌握人類的喜好,他就能夠因應不同的喜好,表現出適當的反應或是良好的互動,減少帶給人類惱怒或是其他負面情緒的機會,同時也增加人類對機器人的接受程度。 | zh_TW |
dc.description.abstract | With the rapid development of robotics, robots have expanded their presence beyond industrial environments and production lines, and have entered daily life. Beside servants, they can be pets, companions, or guides. In the near future, robots will appear in more and more human environments, such as campuses, offices, hospitals, museums and even households. For robots to be useful, and to be accepted by humans, they need to understand human behaviors as well as to adapt to, and relate with, their environments. Human behaviors, however, are highly affected by implicit human factors such as culture, social conventions, laws and even the mental states of individuals and groups. If robots are to be accepted by humans, they must conform to common social norms and local customs as well as recognize highly socialized spatial behaviors.
The main concept of this thesis is to develop the Generalized Spatial Behavior Cognition Model (GSBCM). This model teaches robots how to learn special, implicit rules for various environments. In addition to describing the theory in detail, the thesis provides examples of several scenarios of human environments in which the theory can be applied to practical use. We show that this approach enables robots to accumulate the knowledge needed to ensure good behavior in almost any social situation. The thesis includes a demonstration of a method using the same framework to reason human preferences. Armed with this knowledge, the robot can respond to or interact with humans appropriately, and thus being less likely to cause offence and more likely to be acceptable in society. | en |
dc.description.provenance | Made available in DSpace on 2021-06-12T18:18:13Z (GMT). No. of bitstreams: 1 ntu-100-R98522805-1.pdf: 11719559 bytes, checksum: aea5551f5d7060fa73edd90e9d039717 (MD5) Previous issue date: 2011 | en |
dc.description.tableofcontents | 致謝 i
摘要 ii Abstract iii List of Tables vi List of Figures vii Chapter 1 Introduction 1 1.1 Relations between Humans and Robots 1 1.2 Objectives and Contributions 6 1.3 Structure of Thesis 8 Chapter 2 Background Knowledge 11 2.1 SLAM and Moving Object Tracking 11 2.1.1 Simultaneous Localization and Mapping 12 2.1.2 Moving Object Tracking 15 2.2 Spatial Behavior Cognition Model 17 2.2.1 General Spatial Effect Learning 19 2.2.2 Special Spatial Effect Learning 25 2.3 Pedestrian Ego Graph (PEG) 27 Chapter 3 Generalized Special Behavior Cognition Model 33 3.1 Structure Sketch of the Proposed Model 34 3.2 Cost Functions under GSBCM 36 3.2.1 Cost Functions of GSEs 36 3.2.2 Cost Function of Combination of GSE with SSE 38 3.3 Learning Process for GSBCM 40 3.3.1 GSE Learning Process 41 3.3.2 SSE Learning Process 42 3.3.3 Trajectory Planning 48 3.3.4 Velocity Map 53 Chapter 4 Robot Tells Different Human Preferences 55 4.1 Preference Reasoning by Utilizing GSBCM 55 4.2 SSEs Building Process 58 4.2.1 Introduction to the Scenario 58 4.2.2 SSEs Building for Different Preferences 59 4.3 Real Implementation 61 Chapter 5 Simulations and Experiments 65 5.1 Case Study 1: The Escalator SSE 67 5.1.1 Escalator Conventions 67 5.1.2 Spatial Effect Learning Process for the Robot 68 5.1.3 Simulation Results in Other Environments 70 5.1.4 Balance Adjustment among Velocity Levels 78 5.2 Case Study 2: Robot Knows How to Cross the Street 82 5.2.1 Introduction to the Scenario 82 5.2.2 Traffic Light Duration Recognition 83 5.2.3 Real-world Demonstration 86 5.3 Case Study 3: Robot Behaves Well While People Watching TV 92 5.3.1 Introduction to the Scenario 93 5.3.2 SSE Formation 94 5.3.3 Demonstration in the Hybrid Scenario 98 Chapter 6 Conclusions and Future Works 103 6.1 Conclusions 103 6.2 Future Works 105 References 107 | |
dc.language.iso | en | |
dc.title | 廣義空間行為認知模型與其在智慧型機器人領域之應用 | zh_TW |
dc.title | Generalized Spatial Behavior Cognition Model and Its Applications for Intelligent Robots | en |
dc.type | Thesis | |
dc.date.schoolyear | 99-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 羅仁權(Ren C. Luo),李蔡彥(Tsai-Yen Li) | |
dc.subject.keyword | 行為理解,廣義空間行為認知模型,偏好推測,行動式機器人, | zh_TW |
dc.subject.keyword | Behavior Understanding,GSBCM,Preference Reasoning,Mobile Robot, | en |
dc.relation.page | 112 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2011-08-08 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
顯示於系所單位: | 機械工程學系 |
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