An eikonal equation based path planning method using polygon decomposition and curve evolution
Path planning is a key technique of autonomous navigation for robots, and the velocity field is an important part. Constructing velocity field in a complex workspace is still challenging. In this paper, an inner normal guided segmentation algorithm in a complex polygon is proposed to decompose the c...
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KeAi Communications Co., Ltd.
2020-10-01
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Online Access: | http://www.sciencedirect.com/science/article/pii/S2214914719311894 |
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doaj-01296fd4dbba4339a6bf5b55af1d0fb62021-05-03T01:21:56ZengKeAi Communications Co., Ltd.Defence Technology2214-91472020-10-0116510011018An eikonal equation based path planning method using polygon decomposition and curve evolutionZheng Sun0Zhu-Feng Shao1Hui Li2School of Mechanical and Electrical Engineering, Zaozhuang University, Zaozhuang, 277160, China; Corresponding author.Department of Mechanical Engineering, School of Mechanical Engineering, Tsinghua University, Beijing, 100084, ChinaSchool of Mechanical and Electrical Engineering, Zaozhuang University, Zaozhuang, 277160, ChinaPath planning is a key technique of autonomous navigation for robots, and the velocity field is an important part. Constructing velocity field in a complex workspace is still challenging. In this paper, an inner normal guided segmentation algorithm in a complex polygon is proposed to decompose the complex workspace in this paper. The artificial potential field model based on probability theory is then used to calculate the potential field of the decomposed workspace, and the velocity field is obtained by utilizing the potential field of this workspace. Path optimization is implemented by curve evolution, during which the internal force generated in the smoothing process of the initial path by a mean filter and the external force is obtained from the gradient of the workspace potential field. The parameter selection principle is deduced by analyzing the influence of several parameters on the path length and smoothness. Simulation results show that the designed polygon decomposition algorithm can effectively segment complex workspace and that the path optimization algorithm can shorten and smoothen paths.http://www.sciencedirect.com/science/article/pii/S2214914719311894Level setPath planningArtificial potential fieldPolygon decompositionPath optimizationCurve evolution |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zheng Sun Zhu-Feng Shao Hui Li |
spellingShingle |
Zheng Sun Zhu-Feng Shao Hui Li An eikonal equation based path planning method using polygon decomposition and curve evolution Defence Technology Level set Path planning Artificial potential field Polygon decomposition Path optimization Curve evolution |
author_facet |
Zheng Sun Zhu-Feng Shao Hui Li |
author_sort |
Zheng Sun |
title |
An eikonal equation based path planning method using polygon decomposition and curve evolution |
title_short |
An eikonal equation based path planning method using polygon decomposition and curve evolution |
title_full |
An eikonal equation based path planning method using polygon decomposition and curve evolution |
title_fullStr |
An eikonal equation based path planning method using polygon decomposition and curve evolution |
title_full_unstemmed |
An eikonal equation based path planning method using polygon decomposition and curve evolution |
title_sort |
eikonal equation based path planning method using polygon decomposition and curve evolution |
publisher |
KeAi Communications Co., Ltd. |
series |
Defence Technology |
issn |
2214-9147 |
publishDate |
2020-10-01 |
description |
Path planning is a key technique of autonomous navigation for robots, and the velocity field is an important part. Constructing velocity field in a complex workspace is still challenging. In this paper, an inner normal guided segmentation algorithm in a complex polygon is proposed to decompose the complex workspace in this paper. The artificial potential field model based on probability theory is then used to calculate the potential field of the decomposed workspace, and the velocity field is obtained by utilizing the potential field of this workspace. Path optimization is implemented by curve evolution, during which the internal force generated in the smoothing process of the initial path by a mean filter and the external force is obtained from the gradient of the workspace potential field. The parameter selection principle is deduced by analyzing the influence of several parameters on the path length and smoothness. Simulation results show that the designed polygon decomposition algorithm can effectively segment complex workspace and that the path optimization algorithm can shorten and smoothen paths. |
topic |
Level set Path planning Artificial potential field Polygon decomposition Path optimization Curve evolution |
url |
http://www.sciencedirect.com/science/article/pii/S2214914719311894 |
work_keys_str_mv |
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