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초록·키워드 목차

Motion planning problem is still one of the important issues in robotic applications. In many real-time motion planning problems, it is advisable to find a feasible solution quickly and improve the found solution toward the optimal one before the previously-arranged motion plan ends. For such reasons, sampling-based approaches are becoming popular for real-time application. Especially the use of a rapidly exploring random tree<SUP>*</SUP> (RRT<SUP><SUP>*</SUP></SUP>) algorithm is attractive in real-time application, because it is possible to approach an optimal solution by iterating itself. This paper presents a modified version of informed RRT<SUP>*</SUP> which is an extended version of RRT<SUP>*</SUP> to increase the rate of convergence to optimal solution by improving the sampling method of RRT<SUP>*</SUP>. In online motion planning, the robot plans a path while simultaneously moving along the planned path. Therefore, the part of the path near the robot is less likely to be sampled extensively. For a better solution in online motion planning, we modified the sampling method of informed RRT<SUP>*</SUP> by combining with the sampling method to improve the path nearby robot. With comparison among basic RRT<SUP>*</SUP>, informed RRT<SUP>*</SUP> and the proposed RRT<SUP>*</SUP> in online motion planning, the proposed RRT<SUP>*</SUP> showed the best result by representing the closest solution to optimum. #online motion planning #RRTSUP*/SUP #RRTSUP*/SUP sampling method

Abstract
Ⅰ. 서론
Ⅱ. 알고리즘
Ⅲ. 시뮬레이션
Ⅳ. 결론
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