Sampling-based motion planning of manipulator with goal-oriented sampling

Gitae Kang; Yong Bum Kim; Young Hun Lee; Hyun Seok Oh; Won Suk You; Hyouk Ryeol Choi

doi:10.1007/s11370-019-00281-y

Sampling-based motion planning of manipulator with goal-oriented sampling

Gitae Kang, Yong Bum Kim, Young Hun Lee, Hyun Seok Oh, Won Suk You, Hyouk Ryeol Choi

Department of Mechanical Engineering

Sungkyunkwan University

Research output: Contribution to journal › Article › peer-review

36 Scopus citations

Abstract

A sampling-based planning algorithm is one of the most powerful tools for collision avoidance in the motion planning of manipulators. However, this algorithm takes a long time to generate motions of the manipulator. This work proposes a goal-oriented (GO) sampling method for the motion planning of a manipulator. The GO sampling method can identify the initial solution in a shorter time than other sampling-based algorithms, leading to significant improvement in computational efficiency. Based on the GO sampling method, cases involving configuration space and collision checking are implemented based on the proposed equations in the planning of manipulator motion. Different combinations of configuration space settings are mainly analyzed and compared through experiments using a six-degree-of-freedom manipulator.

Original language	English
Pages (from-to)	265-273
Number of pages	9
Journal	Intelligent Service Robotics
Volume	12
Issue number	3
DOIs	https://doi.org/10.1007/s11370-019-00281-y
State	Published - 1 Jul 2019

Keywords

Manipulator
Obstacle avoidance
Path planning
Rapidly exploring random tree (RRT)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/s11370-019-00281-y

Cite this

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title = "Sampling-based motion planning of manipulator with goal-oriented sampling",

abstract = "A sampling-based planning algorithm is one of the most powerful tools for collision avoidance in the motion planning of manipulators. However, this algorithm takes a long time to generate motions of the manipulator. This work proposes a goal-oriented (GO) sampling method for the motion planning of a manipulator. The GO sampling method can identify the initial solution in a shorter time than other sampling-based algorithms, leading to significant improvement in computational efficiency. Based on the GO sampling method, cases involving configuration space and collision checking are implemented based on the proposed equations in the planning of manipulator motion. Different combinations of configuration space settings are mainly analyzed and compared through experiments using a six-degree-of-freedom manipulator.",

keywords = "Manipulator, Obstacle avoidance, Path planning, Rapidly exploring random tree (RRT)",

author = "Gitae Kang and Kim, \{Yong Bum\} and Lee, \{Young Hun\} and Oh, \{Hyun Seok\} and You, \{Won Suk\} and Choi, \{Hyouk Ryeol\}",

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doi = "10.1007/s11370-019-00281-y",

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AU - Kang, Gitae

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AU - Lee, Young Hun

AU - Oh, Hyun Seok

AU - You, Won Suk

AU - Choi, Hyouk Ryeol

PY - 2019/7/1

Y1 - 2019/7/1

N2 - A sampling-based planning algorithm is one of the most powerful tools for collision avoidance in the motion planning of manipulators. However, this algorithm takes a long time to generate motions of the manipulator. This work proposes a goal-oriented (GO) sampling method for the motion planning of a manipulator. The GO sampling method can identify the initial solution in a shorter time than other sampling-based algorithms, leading to significant improvement in computational efficiency. Based on the GO sampling method, cases involving configuration space and collision checking are implemented based on the proposed equations in the planning of manipulator motion. Different combinations of configuration space settings are mainly analyzed and compared through experiments using a six-degree-of-freedom manipulator.

AB - A sampling-based planning algorithm is one of the most powerful tools for collision avoidance in the motion planning of manipulators. However, this algorithm takes a long time to generate motions of the manipulator. This work proposes a goal-oriented (GO) sampling method for the motion planning of a manipulator. The GO sampling method can identify the initial solution in a shorter time than other sampling-based algorithms, leading to significant improvement in computational efficiency. Based on the GO sampling method, cases involving configuration space and collision checking are implemented based on the proposed equations in the planning of manipulator motion. Different combinations of configuration space settings are mainly analyzed and compared through experiments using a six-degree-of-freedom manipulator.

KW - Manipulator

KW - Obstacle avoidance

KW - Path planning

KW - Rapidly exploring random tree (RRT)

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Sampling-based motion planning of manipulator with goal-oriented sampling

Abstract

Keywords

UN SDGs

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