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Design and implementation of an application scenario-driven A* algorithm on the dynamically self-reconfigurable accelerated arrays.

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Title: Design and implementation of an application scenario-driven A* algorithm on the dynamically self-reconfigurable accelerated arrays.
Authors: BAI, Yulong1 byl202111@163.com, SHAN, Rui1 shanrui0112@163.com
Source: Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Feb2026, Vol. 48 Issue 2, p238-244. 7p.
Subjects: Robotic path planning, Parallel processing, Optimization algorithms, Simulation methods & models, Heuristic
Abstract: In application scenarios of the A* algorithm, when there are few or no obstacles around the parent node, path searching should theoretically become relatively straightforward. However, the A* algorithm still adheres to established rules for node expansion, often leading to unnecessary redundancy in expanding child nodes. To address this issue, this paper proposes an application scenario driven A* algorithm (ASD-A*), which dynamically selects different node expansion step sizes by detecting the number of obstacles near the current node, thereby improving node expansion efficiency. Meanwhile, in response to the flexibly varying node expansion strategy proposed in this paper, a method for parallel implementation of the ASD-A algorithm on the dynamically self-reconfigurable array is introduced to further accelerate the path planning process. Simulation results demonstrate that the ASD-A* algorithm reduces the average time required for path planning by 17.7% compared to the original algorithm across scenarios with varying numbers of obstacles. [ABSTRACT FROM AUTHOR]
Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science 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.)
Database: Engineering Source
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  Data: Design and implementation of an application scenario-driven A* algorithm on the dynamically self-reconfigurable accelerated arrays.
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  Data: <searchLink fieldCode="JN" term="%22Computer+Engineering+%26+Science+%2F+Jisuanji+Gongcheng+yu+Kexue%22">Computer Engineering & Science / Jisuanji Gongcheng yu Kexue</searchLink>. Feb2026, Vol. 48 Issue 2, p238-244. 7p.
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  Data: <searchLink fieldCode="DE" term="%22Robotic+path+planning%22">Robotic path planning</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+processing%22">Parallel processing</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Heuristic%22">Heuristic</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In application scenarios of the A* algorithm, when there are few or no obstacles around the parent node, path searching should theoretically become relatively straightforward. However, the A* algorithm still adheres to established rules for node expansion, often leading to unnecessary redundancy in expanding child nodes. To address this issue, this paper proposes an application scenario driven A* algorithm (ASD-A*), which dynamically selects different node expansion step sizes by detecting the number of obstacles near the current node, thereby improving node expansion efficiency. Meanwhile, in response to the flexibly varying node expansion strategy proposed in this paper, a method for parallel implementation of the ASD-A algorithm on the dynamically self-reconfigurable array is introduced to further accelerate the path planning process. Simulation results demonstrate that the ASD-A* algorithm reduces the average time required for path planning by 17.7% compared to the original algorithm across scenarios with varying numbers of obstacles. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science 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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      – Type: doi
        Value: 10.3969/j.issn.1007-130X.2026.02.005
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      – Code: chi
        Text: Chinese
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      Pagination:
        PageCount: 7
        StartPage: 238
    Subjects:
      – SubjectFull: Robotic path planning
        Type: general
      – SubjectFull: Parallel processing
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
      – SubjectFull: Simulation methods & models
        Type: general
      – SubjectFull: Heuristic
        Type: general
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      – TitleFull: Design and implementation of an application scenario-driven A* algorithm on the dynamically self-reconfigurable accelerated arrays.
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              M: 02
              Text: Feb2026
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              Y: 2026
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