Abstract

Forestry machines have the power to efficiently move very heavy loads, but they are not very smart at communicating information, especially information regarding motion. Understanding how a system produces motion is one of the main stepping stones towards the world of automation. However, to acquire motion data requires sensor hardware that is not largely available in forestry machines today. As a result, at the moment there is no motion data analysis for forestry machines. Therefore, the objective of this article is to present this data, and discuss how we can use such data in regards to technology development. To this end, we have equipped a commercial forestry machine with state-of-the-art sensors and a data acquisition unit. Our aim is to understand what possibilities exist for automation, when we analyze how machine operators control forestry cranes. Among our objectives is to show how motion data can: a) give a better comprehension of the way forestry operators control cranes, b) be useful to analyze crane motion patterns, and c) show additional information that can be estimated via mathematical algorithms. The topics we cover only touch the surface of future applications, where sensor data analysis will be able to team up with other technologies to improve operator's work, including automation, decision making, motion optimization, and operators' training, just to mention some.

Keywords

hydraulic forestry manipulator; data analytics; motion sensors; trajectory planning; boom-tip control; forestry crane joystick control; motion optimization; human-generated motion

Published in

Croatian Journal Of Forest Engineering
2019, Volume: 40, number: 2, pages: 259-280
Publisher: ZAGREB UNIV, FAC FORESTRY

SLU Authors

• La Hera, Pedro

  • Department of Forest Biomaterials and Technology, Swedish University of Agricultural Sciences
    
  • Swedish Cluster of Forest Technology

UKÄ Subject classification

Food Science


Publication identifier

DOI: https://doi.org/10.5552/crojfe.2019.501

Permanent link to this page (URI)

https://res.slu.se/id/publ/102208