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在园艺环境中用于地面机器人的上下文感知导航框架
Peiqi Jin1, Tongxiang Li2, Yaoqiang Pan1
1College of Engineering, South China Agriculture University, Guangzhou 510070, China.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
本研究介绍了使用LiDAR SLAM的园艺机器人自主绘图和导航系统. 该方法增强了语义信息提取,并实现了有效的路径规划,以便在果园中安全和顺利地运行机器人.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 农业工程 农业工程
背景情况:
- 农业中的机器人自动化依赖于准确的环境绘图和导航.
- 现有的方法在高效处理复杂的果园环境方面面临挑战.
研究的目的:
- 开发一种自主地图和导航方法,用于复杂环境中的园艺机器人.
- 提高农业机器人的语义信息提取和路径规划效率.
主要方法:
- 提出了一个基于LiDAR SLAM的语义映射算法,用于分析点云数据和生成路线图.
- 开发了一个通用机器人导航框架,包括全球最短路径规划和本地地形感知路径优化.
- 在一个果园的微分驱动机器人平台上实施和评估了该系统.
主要成果:
- 语义映射算法显著提高了信息提取效率,并将特征提取时间缩短到0.1681s,MIoU为0.812.
- 全球路径规划实现了100%的成功率,平均运行时间为4ms.
- 当地路径规划产生了安全和平稳的轨迹,平均运行时间为36毫秒.
结论:
- 拟议的方法有效地处理果园环境信息,用于自主机器人操作.
- 与现有方法相比,该系统在语义信息提取和路径规划方面表现出卓越的性能.
- 这一进步有助于在农业环境中实现更安全,更有效的机器人自动化.
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