Authors
Takumi Sakamoto1, *, Kensuke Harada1, 2, Weiwei Wan1, 3
1Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Toyonaka 560-8531, Japan
2Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-3-36, Aomi, Koto-ku, Tokyo 135-0064, Japan
3Intelligent Systems Research Institute, Advanced Industrial Science and Technology (AIST), 1-1 Umezono, Tsukuba 305-8560, Japan
*Corresponding author. Email: [email protected]
Corresponding Author
Takumi Sakamoto
Received 7 December 2019, Accepted 18 December 2019, Available Online 29 February 2020.
DOI
https://doi.org/10.2991/jrnal.k.200222.009How to use a DOI?
Keywords
Palletizing; de-palletizing; motion planning; path planning; PRM; RRT*
Abstract
This paper focuses on robotic motion planning for performing the palletizing or de-palletizing tasks. In such tasks, a robot usually iterates similar pick-and-place for several times. Considering such feature of the tasks, we propose two motion planning approaches named reusable Probabilistic Roadmap Method (PRM) and reusable Rapidly-exploring Random Tree Star (RRT*) where both methods utilize the previously constructed roadmaps in the conventional PRM and RRT*, respectively. We experimentally confirm that both methods significantly save the calculation time needed for motion planning compared to the conventional planning methods.
Copyright
© 2020 The Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).