DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Yoon, Sung-Eui | - |
dc.contributor.advisor | 윤성의 | - |
dc.contributor.author | Kim, Dong-Hyuk | - |
dc.date.accessioned | 2021-05-11T19:39:06Z | - |
dc.date.available | 2021-05-11T19:39:06Z | - |
dc.date.issued | 2019 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=871497&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/283322 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 전산학부, 2019.8,[vi, 65 p. :] | - |
dc.description.abstract | Thanks to the probabilistic completeness and almost-sure asymptotic optimality, sampling-based motion planning algorithms have been widely and deeply studied for the past decades. It is, however, a well-known fact that the computational overhead of sampling-based algorithm can dramatically increase with respect to the complexity of a given problem. For this reason, improving the convergence speed toward the optimal solution in motion planning problem is a still-always open problem. In the aspect of computational importance, Sampling, Collision Checking, Nearest Neighbor Search have been considered major components in sampling-based approaches which can greatly affect the resulting random geometric graph. To this context, the thesis of this dissertation is that in order to achieve the faster convergence speed toward the optimal solution we analyze the aforementioned three major components to improve the overall performance of the sampling-based planning, i.e., convergence speed toward the optimal solution. We particularly consider spherical representations to discretize space for various purpose and exploit those for biased-sampling, probabilistic collision checking, and adaptive sparse graph construction. We also hybridize sampling-based and optimized-based planning in conjunction with the spherical representations for faster convergence speed. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Motion planning▼aasymptotic optimality▼afree space approximation▼asampling-based planning▼ahybridization | - |
dc.subject | 모션플래닝▼a점근적 최적성▼a자유공간추정▼a샘플링 기반 플래닝▼a혼성화 | - |
dc.title | Hybridization of sampling-based motion planning algorithms and spherical representations | - |
dc.title.alternative | 샘플링 기반 경로 생성 알고리즘과 구형 표현법의 혼성화 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전산학부, | - |
dc.contributor.alternativeauthor | 김동혁 | - |
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