S-RRT path planning based on slime mould biological model

doi:10.19682/j.cnki.1005-8885.2021.1011

中国邮电高校学报(英文) ›› 2021, Vol. 28 ›› Issue (6): 55-64.doi: 10.19682/j.cnki.1005-8885.2021.1011

• Artificial Intelligence • 上一篇下一篇

S-RRT path planning based on slime mould biological model

You Yue, Li Qinghua, Chen Xiyuan, Zhang Zhao, Mu Yaqi, Feng Chao

1. School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China

2. Jinan Engineering Laboratory of Human-machine Intelligent Cooperation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China

3. School of Electronic and Information Engineering (Department of Physics), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China

4. School of Instrument Science and Engineering, Southeast University,Nanjing 210018, China

5. Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250101, China

收稿日期:2020-12-14 修回日期:2021-07-10 出版日期:2021-12-30 发布日期:2021-12-30
通讯作者: 冯超 E-mail:cfeng@qlu.edu.cn
基金资助:
the National Natural Science Foundation of China (61701270)

S-RRT path planning based on slime mould biological model

1. School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China

2. Jinan Engineering Laboratory of Human-machine Intelligent Cooperation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China

3. School of Electronic and Information Engineering (Department of Physics), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China

4. School of Instrument Science and Engineering, Southeast University,Nanjing 210018, China

5. Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250101, China

Received:2020-12-14 Revised:2021-07-10 Online:2021-12-30 Published:2021-12-30
Supported by:
the National Natural Science Foundation of China (61701270)

摘要/Abstract

摘要：

To improve the security and effectiveness of mobile robot path planning,a slime mould rapid-expansion random tree (S-RRT) algorithm is proposed. This path planning algorithm is designed based on a biological optimization model and a rapid-expansion random tree ( RRT) algorithm. S-RRT algorithm can use the function of optimal direction to constrain the generation of a new node. By controlling the generation direction of the new node, an optimized path can be achieved. Thus, the path oscillation is reduced and the planning time is shortened. It is proved that S-RRT algorithm overcomes the limitation of paths zigzag of RRT algorithm through theoretical analysis. Experiments show that S-RRT algorithm is superior to RRT algorithm in terms of safety and efficiency.

关键词: mobile robot, path planning, rapid-expansion random tree (RRT), slime mould

Abstract: To improve the security and effectiveness of mobile robot path planning,a slime mould rapid-expansion random tree (S-RRT) algorithm is proposed. This path planning algorithm is designed based on a biological optimization model and a rapid-expansion random tree ( RRT) algorithm. S-RRT algorithm can use the function of optimal direction to constrain the generation of a new node. By controlling the generation direction of the new node, an optimized path can be achieved. Thus, the path oscillation is reduced and the planning time is shortened. It is proved that S-RRT algorithm overcomes the limitation of paths zigzag of RRT algorithm through theoretical analysis. Experiments show that S-RRT algorithm is superior to RRT algorithm in terms of safety and efficiency.

Key words: mobile robot, path planning, rapid-expansion random tree (RRT), slime mould

中图分类号:

TP15

You Yue, Li Qinghua, Chen Xiyuan, Zhang Zhao, Mu Yaqi, Feng Chao. S-RRT path planning based on slime mould biological model[J]. The Journal of China Universities of Posts and Telecommunications, 2021, 28(6): 55-64.

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S-RRT path planning based on slime mould biological model

S-RRT path planning based on slime mould biological model

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