Sampling-based optimal motion planning algorithm $Q-RRT^\ast$ and its anytime extension = 샘플링 기반 온라인 최적 모션 계획 알고리즘 $Q-RRT^\ast$와 anytime 확장

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Motion planning is essential part of robotics. Sampling-based algorithms are widely used to solve motion planning problems. In this dissertation, a novel sampling-based motion planning algorithm is proposed to reduce time to find the initial solution and increase the convergence rate. The proposed $Q-RRT^{\ast}$ algorithm increases the convergence rate and finds a solution significantly by preserving the failed samples. The proposed algorithm is analyzed with complexity analysis and numerical simulations. Anytime extension for the proposed algorithm is presented in order to improve the remaining solution while a robot is moving toward the goal. In addition to the anytime extension, a cost functional for safe trajectory preventing collision is also proposed. The effectiveness of the proposed algorithm is verified with simulations and experiments.
Advisors
Kim, Jong-Hwanresearcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2017
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 전기및전자공학부, 2017.2,[v, 62 p. :]

Keywords

Robotics; Motion planning; Sampling-based algorithm; Probabilistic algorithm; Navigation; 로보틱스; 모션 계획; 샘플링 기반 알고리즘; 확률적 알고리즘; 네비게이션

URI
http://hdl.handle.net/10203/242002
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=675800&flag=dissertation
Appears in Collection
EE-Theses_Ph.D.(박사논문)
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