Academic Journal
Enhanced A* path planning with optimized smoothing and shortcutting techniques.
| Τίτλος: | Enhanced A* path planning with optimized smoothing and shortcutting techniques. |
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| Συγγραφείς: | Singh, Raj Kumar1 (AUTHOR) rajphd1@gmail.com, Nagla, Kuldeep Singh1 (AUTHOR) naglaks@nitj.ac.in |
| Πηγή: | Robotic Intelligence & Automation (RIA). 2026, Vol. 46 Issue 3, p361-375. 15p. |
| Θεματικοί όροι: | *Algorithms, *Mathematical optimization, *Heuristic, Robotic path planning |
| Περίληψη: | Purpose: This research aims to enhance the traditional A* algorithm to improve path planning performance in robotics by focusing on generating smoother, shorter and more efficient paths. The key objectives include minimizing path length, reducing sharp turns and producing paths suitable for real-world robotic navigation. Design/methodology/approach: The proposed method integrates a grid-based environment model with a modified A* algorithm, followed by post-processing techniques such as B-spline smoothing, line-of-sight shortcutting and gradient descent optimization. The process involves six main steps: environment modeling, heuristic adjustment, path finding, smoothing, shortcutting and performance tuning. Performance is evaluated based on metrics such as path length, computation time, and success rate using a simulation-based assessment model. Findings: Simulation testing demonstrated that the enhanced algorithm achieved an average path length of 26.4 steps, a computation time of 15.2 ms, and a 100% success rate, indicating significant improvements in efficiency and reliability. Originality/value: This study contributes a hybrid enhancement to the A* algorithm that balances computational efficiency and path quality through a combination of classical and optimization-based techniques. It sets the foundation for future work involving machine learning for adaptive path optimization in dynamic, real-time robotic environments. [ABSTRACT FROM AUTHOR] |
| Copyright of Robotic Intelligence & Automation (RIA) is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Business Source Index |
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| Header | DbId: bsx DbLabel: Business Source Index An: 193315262 RelevancyScore: 1412 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1411.61413574219 |
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| Items | – Name: Title Label: Title Group: Ti Data: Enhanced A* path planning with optimized smoothing and shortcutting techniques. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Singh%2C+Raj+Kumar%22">Singh, Raj Kumar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rajphd1@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Nagla%2C+Kuldeep+Singh%22">Nagla, Kuldeep Singh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> naglaks@nitj.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Robotic+Intelligence+%26+Automation+%28RIA%29%22">Robotic Intelligence & Automation (RIA)</searchLink>. 2026, Vol. 46 Issue 3, p361-375. 15p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Heuristic%22">Heuristic</searchLink><br /><searchLink fieldCode="DE" term="%22Robotic+path+planning%22">Robotic path planning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: This research aims to enhance the traditional A* algorithm to improve path planning performance in robotics by focusing on generating smoother, shorter and more efficient paths. The key objectives include minimizing path length, reducing sharp turns and producing paths suitable for real-world robotic navigation. Design/methodology/approach: The proposed method integrates a grid-based environment model with a modified A* algorithm, followed by post-processing techniques such as B-spline smoothing, line-of-sight shortcutting and gradient descent optimization. The process involves six main steps: environment modeling, heuristic adjustment, path finding, smoothing, shortcutting and performance tuning. Performance is evaluated based on metrics such as path length, computation time, and success rate using a simulation-based assessment model. Findings: Simulation testing demonstrated that the enhanced algorithm achieved an average path length of 26.4 steps, a computation time of 15.2 ms, and a 100% success rate, indicating significant improvements in efficiency and reliability. Originality/value: This study contributes a hybrid enhancement to the A* algorithm that balances computational efficiency and path quality through a combination of classical and optimization-based techniques. It sets the foundation for future work involving machine learning for adaptive path optimization in dynamic, real-time robotic environments. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Robotic Intelligence & Automation (RIA) is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 361 Subjects: – SubjectFull: Algorithms Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Heuristic Type: general – SubjectFull: Robotic path planning Type: general Titles: – TitleFull: Enhanced A* path planning with optimized smoothing and shortcutting techniques. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Singh, Raj Kumar – PersonEntity: Name: NameFull: Nagla, Kuldeep Singh IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 27546969 Numbering: – Type: volume Value: 46 – Type: issue Value: 3 Titles: – TitleFull: Robotic Intelligence & Automation (RIA) Type: main |
| ResultId | 1 |