基于JPS和RRT算法的智能药房无人配送车路线规划和药物识别技术的研究
Hengzhe Yue1, Kun Cao1, Zhaokun Yang1
1Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, 518118, China.
Scientific reports
|December 20, 2025
概括
这项研究通过整合先进的路径规划和识别算法来提高药物分配机器人的效率. 优化的导航和基于轮的药物识别显著改善了药房自动化.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 药房自动化 药房自动化
背景情况:
- 药房机器人中的传统路径规划在准确性和效率方面面临挑战.
- 现有的药物识别方法在动态环境中可能容易出现错误.
研究的目的:
- 为智能药房机器人优化路径规划和药物识别.
- 为了提高机器人的导航精度,定位和药物识别效率.
主要方法:
- 集成的跳跃点搜索 (JPS) 与快速探索的随机树星 (RRT*) 用于路径规划.
- 利用卷积神经网络 (CNN) 进行地图图像匹配和消除错误.
- 开发了一种基于轮的药物识别方法,使用改进的绝对差异和 (SAD) 算法.
主要成果:
- 路径规划时间比A*提高了82%,路径质量比Dijkstra.stra提高了25%.
- 与RRT*相比,CPU使用量减少了68%,在动态环境中具有强大的适应性.
- 改进的SAD算法将药物识别不匹配率降低到15.12%,绝对收益为12.21%,识别时间减少了约7秒.
结论:
- 综合方法显著提高了智能药房中的机器人导航和药物识别精度.
- 优化的算法提高了效率,减少了计算负载,并在复杂的药房环境中保持了稳定性.
- 这项工作有助于更精确,更有效的自动化药物发送系统.
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