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The \(A^{*}\) algorithm is a widely used heuristic for path planning, but it faces challenges of high memory usage and time complexity in large environments. This paper proposes a computational model that integrates Spiking Neural P Systems (SN P Systems) with the \(A^{*}\) algorithm to address these issues. By leveraging the parallel processing capabilities of SN P Systems, the model efficiently computes both the minimal cost function and the optimal path. Firstly, the node movement direction calculation in the \(A^{*}\) algorithm is improved by incorporating SN P Systems, allowing independent subsystem operation to solve the minimal cost function for adjacent nodes through parallel processing. This enhances algorithm efficiency. Secondly, a specialized SN P model recalculates the minimal actual cost when a node appears in the \(A^{*}\) open list. The model is validated using P-Lingua simulation software, confirming its effectiveness. This approach shows promise in improving \(A^{*}\) performance and addressing memory and time complexity in large-scale environments.

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Research on Computational Model of Spiking Neural P System Simulating \(A^*\) Path Planning Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • Suxia Jiang,
  • Yiwen Song,
  • Yanfeng Wang,
  • Chun Huang,
  • Panlong Li

摘要

The \(A^{*}\) algorithm is a widely used heuristic for path planning, but it faces challenges of high memory usage and time complexity in large environments. This paper proposes a computational model that integrates Spiking Neural P Systems (SN P Systems) with the \(A^{*}\) algorithm to address these issues. By leveraging the parallel processing capabilities of SN P Systems, the model efficiently computes both the minimal cost function and the optimal path. Firstly, the node movement direction calculation in the \(A^{*}\) algorithm is improved by incorporating SN P Systems, allowing independent subsystem operation to solve the minimal cost function for adjacent nodes through parallel processing. This enhances algorithm efficiency. Secondly, a specialized SN P model recalculates the minimal actual cost when a node appears in the \(A^{*}\) open list. The model is validated using P-Lingua simulation software, confirming its effectiveness. This approach shows promise in improving \(A^{*}\) performance and addressing memory and time complexity in large-scale environments.