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Adaptive rapidly-exploring random tree for efficient path planning of high-degree-of-freedom articulated robots

Title
Adaptive rapidly-exploring random tree for efficient path planning of high-degree-of-freedom articulated robots
Author
이지영
Keywords
Path planning; motion planning; rapidly-exploring random tree; high degree-of-freedom articulated robot
Issue Date
2015-02
Publisher
SAGE PUBLICATIONS LTD
Citation
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, v. 229, No. 18, Page. 3361-3367
Abstract
This article proposes a method for the path planning of high-degree-of-freedom articulated robots with adaptive dimensionality. For efficient path planning in a high-dimensional configuration space, we first describe an adaptive body selection that selects the robot bodies depending on the complexity of the path planning. Then, the involved joints of the selected body are included in the planning process. That is, it builds the C-space (configuration space) with adaptive dimensionality for sampling-based path planner. Next, by using adaptive body selection, the adaptive rapidly-exploring random tree (RRT) algorithm is introduced, which incrementally grows RRTs in the adaptive dimensional C-space. We show through several simulation results that the proposed method is more efficient than the basic RRT-based path planner, which requires full-dimensional planning.
URI
https://journals.sagepub.com/doi/full/10.1177/0954406215573600https://repository.hanyang.ac.kr/handle/20.500.11754/106023
ISSN
0954-4062; 2041-2983
DOI
10.1177/0954406215573600
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ROBOT ENGINEERING(로봇공학과) > Articles
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