医疗自动引导车辆的避障策略和路径规划基于驼迁徙的生物特征
Jing Hu1, Junchao Niu1, Bangcheng Zhang2
1School of Mechatronical Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Biomimetics (Basel, Switzerland)
|March 26, 2025
概括
这项研究介绍了医院中自动引导车辆 (AGV) 的新生物阻碍回避策略. 这种灵感来自羊的方法提高了实时导航,成功地避免了静态和动态障碍.
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
- 生物模拟技术是生物模拟的
背景情况:
- 自动引导车辆 (AGV) 对于医疗机构的患者运输至关重要.
- AGV 遇到动态和静态障碍的实时挑战,影响效率和安全.
- 现有的导航算法需要对复杂的医疗环境进行增强.
研究的目的:
- 开发和验证医疗环境中AGVs的生物阻碍回避策略.
- 通过整合适应性行为来提高AGV的实时导航能力.
- 为了提高AGV路径规划的成功率和预测范围.
主要方法:
- 改进了传统的人工潜力场和动态窗口算法,使用驼迁移特征.
- 整合了新的潜在现场力量点,以加强导航.
- 增加了动态窗口大小,以改善预测范围.
主要成果:
- 生物战略有效地避免动态和静态障碍同时.
- 实现了34%的路径规划成功率的增加.
- 减少了33%的运行时间和1%的平均路径长度.
结论:
- 拟议的生物阻碍回避策略提高了动态和静态环境中的AGV导航.
- 该方法为医疗保健中高效,安全和快速的AGV操作提供了理论基础.
- 该战略有助于将先进的导航集成到患者运输系统中.
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