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RimJump: Edge-based Shortest Path Planning for a 2D Map

Published online by Cambridge University Press:  29 November 2018

Zhuo Yao Open the ORCID record for Zhuo Yao [Opens in a new window] ,
Weimin Zhang ,
Yongliang Shi ,
Mingzhu Li ,
Zhenshuo Liang ,
Fangxing Li  and
Qiang Huang
Zhuo Yao
Affiliation:
School of Mechatronics, Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, 100081 Beijing, China E-mails: [email protected]; [email protected], [email protected], [email protected], [email protected], [email protected]
Weimin Zhang*
Affiliation:
School of Mechatronics, Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, 100081 Beijing, China E-mails: [email protected]; [email protected], [email protected], [email protected], [email protected], [email protected]
Yongliang Shi
Affiliation:
School of Mechatronics, Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, 100081 Beijing, China E-mails: [email protected]; [email protected], [email protected], [email protected], [email protected], [email protected]
Mingzhu Li
Affiliation:
School of Mechatronics, Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, 100081 Beijing, China E-mails: [email protected]; [email protected], [email protected], [email protected], [email protected], [email protected]
Zhenshuo Liang
Affiliation:
School of Mechatronics, Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, 100081 Beijing, China E-mails: [email protected]; [email protected], [email protected], [email protected], [email protected], [email protected]
Fangxing Li
Affiliation:
School of Mechatronics, Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, 100081 Beijing, China E-mails: [email protected]; [email protected], [email protected], [email protected], [email protected], [email protected]
Qiang Huang
Affiliation:
School of Mechatronics, Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, 100081 Beijing, China E-mails: [email protected]; [email protected], [email protected], [email protected], [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

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Path planning under 2D map is a key issue in robot applications. However, most related algorithms rely on point-by-point traversal. This causes them usually cannot find the strict shortest path, and their time cost increases dramatically as the map scale increases. So we proposed RimJump to solve the above problem, and it is a new path planning method that generates the strict shortest path for a 2D map. RimJump selects points on the edge of barriers to form the strict shortest path. Simulation and experimentation prove that RimJump meets the expected requirements.

Keywords

Path planningShortest pathRimJump
Type
Articles
Information
Robotica , Volume 37 , Issue 4 , April 2019 , pp. 641 - 655
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © Cambridge University Press 2018

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