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<p>This paper is set in the context of an intelligent pharmacy working environment, where we optimize the conventional path planning algorithms for existing drug-dispensing robots. By integrating the Jump Point Search algorithm with the Rapidly-exploring Random Tree Star algorithm, we enhance the accuracy of robot navigation and positioning.Utilizing CNN to fit the map images and setting thresholds to eliminate errors, thereby improving the accuracy and robustness of the map; optimizing the path algorithm to prevent local optima and deadlocks. We introduce a contour-based drug recognition method that uses the SAD algorithm for accurate identification of drug types and positions, ensuring high precision and efficiency during delivery. This ensures high precision and efficiency in drug recognition during the delivery process. This method improves planning time by 82% over A*, enhances path quality by 25% over Dijkstra, and reduces CPU usage by 68% compared with RRT*, while maintaining strong adaptability in dynamic environments. For drug recognition, the improved SAD algorithm lowers the mismatch rate from 27.33% (SAD) and 25.92% (SSD) to 15.12% on a 500-image dataset collected under mixed indoor lighting, achieving a 12.21% absolute gain and reducing recognition time by about 7 seconds. These values were measured on a 500-image pharmacy dataset collected under mixed indoor lighting.</p>

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Research on intelligent pharmacy unmanned delivery vehicle path planning and drug recognition technology based on JPS and RRT algorithms

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月20日

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  • Hengzhe Yue,
  • Kun Cao,
  • Zhaokun Yang,
  • Quntao An,
  • Lizhi Sun

摘要

This paper is set in the context of an intelligent pharmacy working environment, where we optimize the conventional path planning algorithms for existing drug-dispensing robots. By integrating the Jump Point Search algorithm with the Rapidly-exploring Random Tree Star algorithm, we enhance the accuracy of robot navigation and positioning.Utilizing CNN to fit the map images and setting thresholds to eliminate errors, thereby improving the accuracy and robustness of the map; optimizing the path algorithm to prevent local optima and deadlocks. We introduce a contour-based drug recognition method that uses the SAD algorithm for accurate identification of drug types and positions, ensuring high precision and efficiency during delivery. This ensures high precision and efficiency in drug recognition during the delivery process. This method improves planning time by 82% over A*, enhances path quality by 25% over Dijkstra, and reduces CPU usage by 68% compared with RRT*, while maintaining strong adaptability in dynamic environments. For drug recognition, the improved SAD algorithm lowers the mismatch rate from 27.33% (SAD) and 25.92% (SSD) to 15.12% on a 500-image dataset collected under mixed indoor lighting, achieving a 12.21% absolute gain and reducing recognition time by about 7 seconds. These values were measured on a 500-image pharmacy dataset collected under mixed indoor lighting.