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

샘플링 기법의 보완을 통한 RRT* 기반 온라인 이동 계획의 성능 개선

  • Lee, Hee Beom (School of Mechanical and Aerospace, Seoul National University) ;
  • Kwak, HwyKuen (Command & Control Group, Hanwha Thales) ;
  • Kim, JoonWon (Command & Control Group, Hanwha Thales) ;
  • Lee, ChoonWoo (Command & Control Group, Hanwha Thales) ;
  • Kim, H.Jin (School of Mechanical and Aerospace, Seoul National University)
  • 이희범 (서울대학교 기계항공공학부 항공우주신기술연구소) ;
  • 곽휘권 (한화탈레스 지휘통제팀) ;
  • 김준원 (한화탈레스 지휘통제팀) ;
  • 이춘우 (한화탈레스 지휘통제팀) ;
  • 김현진 (서울대학교 기계항공공학부 항공우주신기술연구소)
  • Received : 2015.08.13
  • Accepted : 2016.01.11
  • Published : 2016.03.01

Abstract

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.

Keywords

References

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Cited by

  1. Improved Path Planning Algorithm based on Informed RRT* using Gridmap Skeletonization vol.13, pp.2, 2018, https://doi.org/10.7746/jkros.2018.13.2.142