期刊
AUTOMATION IN CONSTRUCTION
卷 119, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.autcon.2020.103374
关键词
Material classification; Force sensing; Machine learning; Robotic excavation; Loader automation
资金
- Epiroc AB
- NSERC Project RGPIN [2015-04025]
- NSERC Canadian Robotics Network (NCRN)
The ability of robotic excavators to acquire meaningful knowledge about materials during digging can augment their autonomous functionality, as well as optimize downstream operations in construction and mining. Some material properties, such as rock sizes, can be determined visually, but these methods cannot see what lies beneath. In this work, a classification methodology that utilizes only proprioceptive force data acquired from an autonomous digging system and machine learning algorithms is proposed for excavation material identification. The consistent performance synonymous with autonomous digging systems allows for the use of basic features extracted from the force data for classification. A proof of concept of this novel approach to excavation material classification is demonstrated through a binary classification of rock and gravel materials. Force data were obtained from full-scale autonomous loading trials with a 14-tonne capacity load-haul-dump machine at a mining and construction test facility. Preliminary results achieved a classification accuracy of 90%.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据