La Hera Pedro, PhD.

Estimating the Position of the Harvester Head – a Key Step towards the Precision Forestry of the Future?

volume: 36, issue: 2

Drivers of Advances in Mechanized Timber Harvesting – a Selective Review of Technological Innovation

volume: 38, issue: 2

What Do We Observe When We Equip a Forestry Crane with Motion Sensors?

volume: 40, issue:

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.

Exploring the Design of Highly Energy Efficient Forestry Cranes using Gravity Compensation

volume: 43, issue:

Although most mechanized forestry work relies heavily on cranes for handling logs along the supply chain, there has been little research on how to improve cranes design. In addition, the available research has mainly focused on improving current designs, so there is a lack of application of modern methods for designing cranes with improved efficiency.

This paper analyzes how a mechanical engineering design method, known as gravity compensation, can be used to make a new generation of highly energy efficient forestry cranes. To introduce this design approach, a standard forwarder crane with two booms is used as a model system on which to apply gravity compensation concepts. The design methodology follows a procedure based on physics and mathematical optimization, with the objective of minimizing the energy needed to move the crane by using gravity compensation via counterweights. To this end, we considered to minimize mechanical power, because this quantity relates to how fuel and hydraulic fluid are converted into mechanical motion.

This analysis suggests that using gravity compensation could reduce energy consumption due to crane work by 27%, at the cost of increasing the crane total mass by 57%. Thus, the original crane mass of 559 kg increases to 879 kg after applying gravity compensation with counterweights. However, overall reductions in energy consumption would depend on both the crane work and the extraction distance. The greater the extraction distance, the lower the total savings. However, energy consumption savings of around 2% could be achieved even with an extraction distance of 1 km.

From a design perspective, this study emphasized the need to consider gravity compensation in the design philosophy of forestry cranes, not only for its ability to minimize energy consumption, but also due to all the inherited properties it provides. This initial study concludes that designing cranes with a combination of gravity compensation concepts could yield a new generation of highly energy efficient cranes with energy savings exceeding those reported here.


Web of Science Impact factor (2022): 3.200
Five-years impact factor: 3.000

Quartile: Q1 - Forestry

Subject area

Agricultural and Biological Sciences