An Automated Guided Vehicle Path Planning Algorithm Based on Improved A* and Dynamic Window Approach Fusion
Aimed at the problems of low search efficiency of the A* algorithm in global path planning, not considering the size of AGV and too many turns, and the DWA algorithm easily falling into local optimization, an AGV path planning algorithm based on improved A* and DWA fusion is proposed. To begin, the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/18/10326 |
| _version_ | 1797581388274204672 |
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| author | Tao Guo Yunquan Sun Yong Liu Li Liu Jing Lu |
| author_facet | Tao Guo Yunquan Sun Yong Liu Li Liu Jing Lu |
| author_sort | Tao Guo |
| collection | DOAJ |
| description | Aimed at the problems of low search efficiency of the A* algorithm in global path planning, not considering the size of AGV and too many turns, and the DWA algorithm easily falling into local optimization, an AGV path planning algorithm based on improved A* and DWA fusion is proposed. To begin, the obstacle rate coefficient is added to the A* algorithm’s evaluation function to build an adaptive cost function; the search efficiency and path safety are increased by improving the search mode; by extracting key nodes, a global path containing only the starting point, key nodes, and endpoints is obtained. The DWA algorithm’s evaluation function is then optimized and the starting azimuth is optimized based on information from the first key node. The experimental results show that in a static environment, compared with the traditional A* algorithm and the improved A* algorithm, the path length is reduced by 1.3% and 5.6%, respectively, and the turning times are reduced by 62.5% and 70%, respectively; compared with the improved ant colony algorithm in the literature, the turning angle is reduced by 29%. In the dynamic environment, the running time of this fusion algorithm is reduced by 12.6% compared with the other hybrid algorithms. |
| first_indexed | 2024-03-10T23:04:32Z |
| format | Article |
| id | doaj.art-a5631531202f4541a4ff632467e4f7a5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:04:32Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a5631531202f4541a4ff632467e4f7a52023-11-19T09:26:12ZengMDPI AGApplied Sciences2076-34172023-09-0113181032610.3390/app131810326An Automated Guided Vehicle Path Planning Algorithm Based on Improved A* and Dynamic Window Approach FusionTao Guo0Yunquan Sun1Yong Liu2Li Liu3Jing Lu4School of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644000, ChinaAcademy for Engineering & Technology, Fudan University, Shanghai 200000, ChinaSchool of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644000, ChinaSchool of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644000, ChinaSchool of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644000, ChinaAimed at the problems of low search efficiency of the A* algorithm in global path planning, not considering the size of AGV and too many turns, and the DWA algorithm easily falling into local optimization, an AGV path planning algorithm based on improved A* and DWA fusion is proposed. To begin, the obstacle rate coefficient is added to the A* algorithm’s evaluation function to build an adaptive cost function; the search efficiency and path safety are increased by improving the search mode; by extracting key nodes, a global path containing only the starting point, key nodes, and endpoints is obtained. The DWA algorithm’s evaluation function is then optimized and the starting azimuth is optimized based on information from the first key node. The experimental results show that in a static environment, compared with the traditional A* algorithm and the improved A* algorithm, the path length is reduced by 1.3% and 5.6%, respectively, and the turning times are reduced by 62.5% and 70%, respectively; compared with the improved ant colony algorithm in the literature, the turning angle is reduced by 29%. In the dynamic environment, the running time of this fusion algorithm is reduced by 12.6% compared with the other hybrid algorithms.https://www.mdpi.com/2076-3417/13/18/10326AGV path planningDWA algorithmfusion algorithmimproved A* algorithm |
| spellingShingle | Tao Guo Yunquan Sun Yong Liu Li Liu Jing Lu An Automated Guided Vehicle Path Planning Algorithm Based on Improved A* and Dynamic Window Approach Fusion Applied Sciences AGV path planning DWA algorithm fusion algorithm improved A* algorithm |
| title | An Automated Guided Vehicle Path Planning Algorithm Based on Improved A* and Dynamic Window Approach Fusion |
| title_full | An Automated Guided Vehicle Path Planning Algorithm Based on Improved A* and Dynamic Window Approach Fusion |
| title_fullStr | An Automated Guided Vehicle Path Planning Algorithm Based on Improved A* and Dynamic Window Approach Fusion |
| title_full_unstemmed | An Automated Guided Vehicle Path Planning Algorithm Based on Improved A* and Dynamic Window Approach Fusion |
| title_short | An Automated Guided Vehicle Path Planning Algorithm Based on Improved A* and Dynamic Window Approach Fusion |
| title_sort | automated guided vehicle path planning algorithm based on improved a and dynamic window approach fusion |
| topic | AGV path planning DWA algorithm fusion algorithm improved A* algorithm |
| url | https://www.mdpi.com/2076-3417/13/18/10326 |
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