Improved Exponential and Cost-Weighted Hybrid Algorithm for Mobile Robot Path Planning
The A* algorithm is widely used in mobile robot path planning; however, it faces challenges such as unsmooth planned paths, redundant nodes, and extensive search areas. This paper proposes a hybrid algorithm combining an improved A* algorithm with the Dynamic Window Approach. By quantifying grid obs...
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MDPI AG
2025-04-01
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| author | Ming Hu Shuhai Jiang Kangqian Zhou Xunan Cao Cun Li |
| author_facet | Ming Hu Shuhai Jiang Kangqian Zhou Xunan Cao Cun Li |
| author_sort | Ming Hu |
| collection | DOAJ |
| description | The A* algorithm is widely used in mobile robot path planning; however, it faces challenges such as unsmooth planned paths, redundant nodes, and extensive search areas. This paper proposes a hybrid algorithm combining an improved A* algorithm with the Dynamic Window Approach. By quantifying grid obstacle data to extract environmental information and employing a grid-based environmental modeling method, the proposed approach enhances path smoothness at turns using second-order Bezier curve smoothing. It improves the heuristic function and child node selection process, applying these advancements in experimental path planning scenarios. A simulated 2D map was constructed using point cloud scanning in RViz to validate the hybrid algorithm through simulations and real-world outdoor tests. Experimental results demonstrate that, compared to the A* and DWA algorithms, the improved hybrid algorithm enhances search efficiency by 10.93%, reduces search node count by 32.26%, decreases the number of turning points by 36.36% and the value of turning angle by 34.83%, shortens the total path length by 22.05%, and improves overall path smoothness. Simulations and field tests confirm that the proposed hybrid algorithm is more stable, significantly reduces collision probability, and demonstrates its applicability for mobile robot localization and navigation in real-world environments. |
| format | Article |
| id | doaj-art-bf342de640d24fc2bd2f64d9ee74960b |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-bf342de640d24fc2bd2f64d9ee74960b2025-08-20T03:13:45ZengMDPI AGSensors1424-82202025-04-01258257910.3390/s25082579Improved Exponential and Cost-Weighted Hybrid Algorithm for Mobile Robot Path PlanningMing Hu0Shuhai Jiang1Kangqian Zhou2Xunan Cao3Cun Li4School of Mechanical and Electronic Engineering, Nanjing Forestry University, 159 Longpan Road, Nanjing 210037, ChinaSchool of Mechanical and Electronic Engineering, Nanjing Forestry University, 159 Longpan Road, Nanjing 210037, ChinaSchool of Mechanical and Electronic Engineering, Nanjing Forestry University, 159 Longpan Road, Nanjing 210037, ChinaSchool of Mechanical and Electronic Engineering, Nanjing Forestry University, 159 Longpan Road, Nanjing 210037, ChinaSchool of Mechanical and Electronic Engineering, Nanjing Forestry University, 159 Longpan Road, Nanjing 210037, ChinaThe A* algorithm is widely used in mobile robot path planning; however, it faces challenges such as unsmooth planned paths, redundant nodes, and extensive search areas. This paper proposes a hybrid algorithm combining an improved A* algorithm with the Dynamic Window Approach. By quantifying grid obstacle data to extract environmental information and employing a grid-based environmental modeling method, the proposed approach enhances path smoothness at turns using second-order Bezier curve smoothing. It improves the heuristic function and child node selection process, applying these advancements in experimental path planning scenarios. A simulated 2D map was constructed using point cloud scanning in RViz to validate the hybrid algorithm through simulations and real-world outdoor tests. Experimental results demonstrate that, compared to the A* and DWA algorithms, the improved hybrid algorithm enhances search efficiency by 10.93%, reduces search node count by 32.26%, decreases the number of turning points by 36.36% and the value of turning angle by 34.83%, shortens the total path length by 22.05%, and improves overall path smoothness. Simulations and field tests confirm that the proposed hybrid algorithm is more stable, significantly reduces collision probability, and demonstrates its applicability for mobile robot localization and navigation in real-world environments.https://www.mdpi.com/1424-8220/25/8/2579hybrid algorithmdynamic window approachpath planningnavigation |
| spellingShingle | Ming Hu Shuhai Jiang Kangqian Zhou Xunan Cao Cun Li Improved Exponential and Cost-Weighted Hybrid Algorithm for Mobile Robot Path Planning Sensors hybrid algorithm dynamic window approach path planning navigation |
| title | Improved Exponential and Cost-Weighted Hybrid Algorithm for Mobile Robot Path Planning |
| title_full | Improved Exponential and Cost-Weighted Hybrid Algorithm for Mobile Robot Path Planning |
| title_fullStr | Improved Exponential and Cost-Weighted Hybrid Algorithm for Mobile Robot Path Planning |
| title_full_unstemmed | Improved Exponential and Cost-Weighted Hybrid Algorithm for Mobile Robot Path Planning |
| title_short | Improved Exponential and Cost-Weighted Hybrid Algorithm for Mobile Robot Path Planning |
| title_sort | improved exponential and cost weighted hybrid algorithm for mobile robot path planning |
| topic | hybrid algorithm dynamic window approach path planning navigation |
| url | https://www.mdpi.com/1424-8220/25/8/2579 |
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