dc.contributor.advisor | 李蔡彥 | zh_TW |
dc.contributor.advisor | Li,Tsai-Yen | en_US |
dc.contributor.author (Authors) | 黃培智 | zh_TW |
dc.contributor.author (Authors) | Huang,Pei-Zhi | en_US |
dc.creator (作者) | 黃培智 | zh_TW |
dc.creator (作者) | Huang,Pei-Zhi | en_US |
dc.date (日期) | 2003 | en_US |
dc.date.accessioned | 17-Sep-2009 13:53:56 (UTC+8) | - |
dc.date.available | 17-Sep-2009 13:53:56 (UTC+8) | - |
dc.date.issued (上傳時間) | 17-Sep-2009 13:53:56 (UTC+8) | - |
dc.identifier (Other Identifiers) | G0091753002 | en_US |
dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/32633 | - |
dc.description (描述) | 碩士 | zh_TW |
dc.description (描述) | 國立政治大學 | zh_TW |
dc.description (描述) | 資訊科學學系 | zh_TW |
dc.description (描述) | 91753002 | zh_TW |
dc.description (描述) | 92 | zh_TW |
dc.description.abstract (摘要) | Research on global path planning and navigation strategies for mobile robots has been well studied in the robotics literature. Since the problem can usually be modeled as searching for a collision-free path in a 2D workspace, very efficient and complete algorithms can be employed. However, enabling a humanoid robot to move autonomously in a real-life environment remains a challenging problem. Unlike traditional wheeled robots, legged robots such as humanoid robots have advanced abilities of stepping over an object or striding over a deep gap with versatile locomotions. In this thesis, we propose a motion planning system capable of generating both global and local motions for a humanoid robot in layered environment cluttered with obstacles and deep narrow gaps. The planner can generate a gross motion that takes multiple locomotions, humanoid’s geometric properties and striding ability into consideration. A gross motion plan that satisfies the constraints is generated and further realized by a local planner, which determines the most efficient footsteps and locomotion over uneven terrain. If the local planner fails, the failure is fed back to the global planner to consider other alternative paths. The experiments show that our system can efficiently generate humanoid motions to reach the goal in a real-life environment. The system can also apply to a real humanoid robot to provide a high-level control mechanism. | zh_TW |
dc.description.tableofcontents | 1 Introduction ....................................................................................................11.1 Motivations ..............................................................................................21.2 Autonomous Digital Actor .......................................................................31.3 Realism vs. Interactivity ..........................................................................41.4 Contributions ...........................................................................................51.5 Mathematical Formulation ......................................................................61.6 Organization ............................................................................................72 Related Work ..................................................................................................82.1 Gross Motion Planning ............................................................................92.2 Character Animation ..............................................................................102.3 Two-Level Motion Planning ..................................................................112.3.1 Low DOF Workspace Approach .................................................122.3.2 Randomized Approach ...............................................................133 Problem Description .....................................................................................143.1 Overall Problem Description .................................................................143.2 Virtual Human Modeling ................................................................163.3 Global Motion Planning Problem ..........................................................183.4 Local Motion Planning Problem ............................................................203.5 Replanning Problem ..............................................................................224 Environment Modeling .................................................................................244.1 Binary Bitmap Representation ...............................................................254.2 Height Map Representation ...................................................................264.3 Layered Height Map Representation .....................................................274.4 Merging Sparse Layers Representation .................................................295 Reachability and Collision Modeling ...........................................................335.1 Virtual human’s Kinematic and Geometric Properties ..........................345.2 Locomotion Ability ................................................................................375.2.1 Extend the Reachable Region In Search ....................................395.2.2 Extend the Reachable Region In Preprocessing .........................395.3 Instability ...............................................................................................425.4 Static Collision Detection ......................................................................445.5 Reachability Map with Multiple Locomotion .......................................466 Global Path Searching ..................................................................................516.1 Potential Field ........................................................................................526.2 Path Planning Algorithm .......................................................................546.3 Dynamic Collision Detection ................................................................566.4 Search Criteria .......................................................................................576.5 Post Processing ......................................................................................587 Replanning ....................................................................................................607.1 Fully Reconstruct Approach ..................................................................607.2 Partial Update Approach ........................................................................638 Experimental Results ..............................................................................668.1 Experiments with Different Step Height ...............................................668.2 Experiments with Different Preferences ................................................688.3 Effect of Virtual Human’s Height ..........................................................708.4 Stair-like Terrain Examples ...................................................................728.5 Layered Environment Examples ...........................................................748.6 Replanning Examples ............................................................................748.7 Global Planning with Striding Ability ...................................................778.8 Versatile Locomotion Examples ............................................................799 Conclusions and Future Work ......................................................................839.1 Summary ................................................................................................839.2 Discussion ..............................................................................................84References ..........................................................................................................86 | zh_TW |
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dc.language.iso | en_US | - |
dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0091753002 | en_US |
dc.subject (關鍵詞) | Motion Planning | en_US |
dc.subject (關鍵詞) | Humanoid | en_US |
dc.subject (關鍵詞) | Animation | en_US |
dc.title (題名) | 自動產生具多樣化運動的虛擬人物動畫 | zh_TW |
dc.title (題名) | Generating Humanoid Animation with Versatile Motions in a Virtual Environment | en_US |
dc.type (資料類型) | thesis | en |
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