Geometric A-Star Algorithm: An Improved A-Star Algorithm for AGV Path Planning in a Port Environment
This research introduces a path planning method based on the geometric A-star algorithm. The whole approach is applied to an Automated Guided Vehicle (AGV) in order to avoid the problems of many nodes, long-distance and large turning angle, and these problems usually exist in the sawtooth and cross...
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| Published in | IEEE access Vol. 9; pp. 59196 - 59210 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Piscataway
IEEE
2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2169-3536 2169-3536 |
| DOI | 10.1109/ACCESS.2021.3070054 |
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| Abstract | This research introduces a path planning method based on the geometric A-star algorithm. The whole approach is applied to an Automated Guided Vehicle (AGV) in order to avoid the problems of many nodes, long-distance and large turning angle, and these problems usually exist in the sawtooth and cross paths produced by the traditional A-star algorithm. First, a grid method models a port environment. Second, the nodes in the close-list are filtered by the functions <inline-formula> <tex-math notation="LaTeX">P\left ({{x,y} }\right) </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">W\left ({{x,y} }\right) </tex-math></inline-formula> and the nodes that do not meet the requirements are removed to avoid the generation of irregular paths. Simultaneously, to enhance the stability of the AGV regarding turning paths, the polyline at the turning path is replaced by a cubic B-spline curve. The path planning experimental results applied to different scenarios and different specifications showed that compared with other seven different algorithms, the geometric A-star algorithm reduces the number of nodes by 10% ~ 40%, while the number of turns is reduced by 25%, the turning angle is reduced by 33.3%, and the total distance is reduced by 25.5%. Overall, the simulation results of the path planning confirmed the effectiveness of the geometric A-star algorithm. |
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| AbstractList | This research introduces a path planning method based on the geometric A-star algorithm. The whole approach is applied to an Automated Guided Vehicle (AGV) in order to avoid the problems of many nodes, long-distance and large turning angle, and these problems usually exist in the sawtooth and cross paths produced by the traditional A-star algorithm. First, a grid method models a port environment. Second, the nodes in the close-list are filtered by the functions <inline-formula> <tex-math notation="LaTeX">P\left ({{x,y} }\right) </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">W\left ({{x,y} }\right) </tex-math></inline-formula> and the nodes that do not meet the requirements are removed to avoid the generation of irregular paths. Simultaneously, to enhance the stability of the AGV regarding turning paths, the polyline at the turning path is replaced by a cubic B-spline curve. The path planning experimental results applied to different scenarios and different specifications showed that compared with other seven different algorithms, the geometric A-star algorithm reduces the number of nodes by 10% ~ 40%, while the number of turns is reduced by 25%, the turning angle is reduced by 33.3%, and the total distance is reduced by 25.5%. Overall, the simulation results of the path planning confirmed the effectiveness of the geometric A-star algorithm. This research introduces a path planning method based on the geometric A-star algorithm. The whole approach is applied to an Automated Guided Vehicle (AGV) in order to avoid the problems of many nodes, long-distance and large turning angle, and these problems usually exist in the sawtooth and cross paths produced by the traditional A-star algorithm. First, a grid method models a port environment. Second, the nodes in the close-list are filtered by the functions <tex-math notation="LaTeX">$P\left ({{x,y} }\right)$ </tex-math> and <tex-math notation="LaTeX">$W\left ({{x,y} }\right)$ </tex-math> and the nodes that do not meet the requirements are removed to avoid the generation of irregular paths. Simultaneously, to enhance the stability of the AGV regarding turning paths, the polyline at the turning path is replaced by a cubic B-spline curve. The path planning experimental results applied to different scenarios and different specifications showed that compared with other seven different algorithms, the geometric A-star algorithm reduces the number of nodes by 10% ~ 40%, while the number of turns is reduced by 25%, the turning angle is reduced by 33.3%, and the total distance is reduced by 25.5%. Overall, the simulation results of the path planning confirmed the effectiveness of the geometric A-star algorithm. This research introduces a path planning method based on the geometric A-star algorithm. The whole approach is applied to an Automated Guided Vehicle (AGV) in order to avoid the problems of many nodes, long-distance and large turning angle, and these problems usually exist in the sawtooth and cross paths produced by the traditional A-star algorithm. First, a grid method models a port environment. Second, the nodes in the close-list are filtered by the functions [Formula Omitted] and [Formula Omitted] and the nodes that do not meet the requirements are removed to avoid the generation of irregular paths. Simultaneously, to enhance the stability of the AGV regarding turning paths, the polyline at the turning path is replaced by a cubic B-spline curve. The path planning experimental results applied to different scenarios and different specifications showed that compared with other seven different algorithms, the geometric A-star algorithm reduces the number of nodes by 10% ~ 40%, while the number of turns is reduced by 25%, the turning angle is reduced by 33.3%, and the total distance is reduced by 25.5%. Overall, the simulation results of the path planning confirmed the effectiveness of the geometric A-star algorithm. |
| Author | Tang, Gang Claramunt, Christophe Hu, Xiong Tang, Congqiang Zhou, Peipei |
| Author_xml | – sequence: 1 givenname: Gang orcidid: 0000-0002-8706-4431 surname: Tang fullname: Tang, Gang organization: School of Logistics Engineering, Shanghai Maritime University, Shanghai, China – sequence: 2 givenname: Congqiang orcidid: 0000-0002-0282-1609 surname: Tang fullname: Tang, Congqiang organization: School of Logistics Engineering, Shanghai Maritime University, Shanghai, China – sequence: 3 givenname: Christophe orcidid: 0000-0002-5586-1997 surname: Claramunt fullname: Claramunt, Christophe organization: School of Logistics Engineering, Shanghai Maritime University, Shanghai, China – sequence: 4 givenname: Xiong surname: Hu fullname: Hu, Xiong organization: School of Logistics Engineering, Shanghai Maritime University, Shanghai, China – sequence: 5 givenname: Peipei orcidid: 0000-0002-6293-543X surname: Zhou fullname: Zhou, Peipei email: zhoupeipei@gpnu.edu.cn organization: School of Mechatronic Engineering, Guangdong Polytechnic Normal University, Guangzhou, China |
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| SubjectTerms | A-star algorithm Algorithms automated guided vehicle (AGV) Automated guided vehicles B spline functions Computational modeling Environment models Grid method Heuristic algorithms Layout Nodes Path planning Satellites Splines (mathematics) Turning |
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| Title | Geometric A-Star Algorithm: An Improved A-Star Algorithm for AGV Path Planning in a Port Environment |
| URI | https://ieeexplore.ieee.org/document/9391698 https://www.proquest.com/docview/2517034096 https://doaj.org/article/a3e6b9c0bbec4053a524064914fb084b |
| Volume | 9 |
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