使用当前值中位数的数据聚类利用技术,以改善非结构化环境中的弧度传感
Hee-Jun Kim1,2, Jeong-Ho Kim2,3, Shin-Nyeong Heo1
1Samsung Heavy Industries Co., Ltd., Geoje-si 53261, Republic of Korea.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
本研究引入了一种新的基于中位数的空间聚类 (MBSC) 算法,以改进机器人接接跟踪. MBSC算法在具有曲工件的具有挑战性的造船环境中提高了准确性和响应性.
科学领域:
- 机器人和自动化机器人与自动化
- 材料科学与工程 材料科学与工程
- 制造过程 制造过程 制造过程
背景情况:
- 在造船业的接自动化中,由于空间限制,经常使用弧度传感.
- 弧度传感中的短路过渡降低了曲或隙工件的反电流可靠性,导致跟踪故障和损坏.
研究的目的:
- 开发一个强大的跟踪算法,用于机器人接在具有挑战性的造船环境.
- 为了提高接机器人处理工件曲率和空隙的准确性和响应性.
主要方法:
- 提出了一种新的算法:基于中位数的空间聚类 (MBSC).
- MBSC基于DBSCAN (基于密度的应用程序与噪音的空间聚类) 聚类算法.
- 该算法基于编织区域内的中位数集群数据,并分析反当前数据特征,使用异常值来改进跟踪.
主要成果:
- 该MBSC算法证明了增强的跟踪精度.
- 在非结构化和具有挑战性的接场景中观察到更好的响应能力.
- 通过在造船厂的现实接实验来验证有效性.
结论:
- 对于机器人接接跟踪的传统方法,MBSC算法提供了显著的改进.
- 该技术在克服工件曲率和空隙所带来的挑战方面尤其有效.
- 这一进步有助于在造船领域实现更可靠,更无损的自动接.
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