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샘플링 기법의 보완을 통한 RRT<SUP>*</SUP> 기반 온라인 이동 계획의 성능 개선

  • 학술저널

샘플링 기법의 보완을 통한 RRT<SUP>*</SUP> 기반 온라인 이동 계획의 성능 개선

Improvement of Online Motion Planning based on RRT* by Modification of the Sampling Method

이희범(서울대학교) 곽휘권(한화탈레스) 김준원(한화탈레스) 이춘우(한화탈레스) 김현진(서울대학교)

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초록

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* (RRT*) 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* which is an extended version of RRT* to increase the rate of convergence to optimal solution by improving the sampling method of RRT*. 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* by combining with the sampling method to improve the path nearby robot. With comparison among basic RRT*, informed RRT* and the proposed RRT* in online motion planning, the proposed RRT* showed the best result by representing the closest solution to optimum.

목차

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

참고문헌(9)

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  • 2.

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  • 3.

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  • 4.

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  • 5.

    Karaman, Sertac, and Emilio Frazzoli, “Incremental sam- pling-based algorithms for optimal motion planning,” arXiv preprint arXiv:1005.0416, May 2010.

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