Path-Constrained Motion Analysis: An Algorithm to Understand Human Performance on Hydraulic Manipulators

Morales, Daniel Ortiz; Hera, Pedro La; Westerberg, Simon; Freidovich, Leonid B.; Shiriaev, Anton S.

doi:10.1109/thms.2014.2366873

Cited by 18 publications

(22 citation statements)

References 19 publications

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“…However, we would like to emphasize that the analysis we present is not extensive, because the purpose is to highlight some of the key aspects of motion data analysis. Interested readers can find more complete examples of how we have used similar data in the area of robotics (La Hera and Morales 2015), machine learning (Ortiz Morales et al 2014), and automatic analysis of human-operator performance (Morales et al 2015), which are research results that include other development cases.…”

Section: Purpose Of This Articlementioning

confidence: 99%

“…Unfortunately, calculating the energy consumed during motion falls out of the scope of the present article, because measuring energy requires extra set of sensors. Nevertheless, some preliminary work along these concepts can be found in literature (Morales et al 2015).…”

Section: Crane Motion Patternsmentioning

confidence: 99%

“…However, despite the differences, both operators show working cycles of nearly similar time. Morales et al (2015) explained how the work with forestry cranes could be speeded up using automation software. According to Morales et al (2015), time-efficiency could increase by at least three-fold if done by an autonomous control system.…”

Section: Crane Velocities Showing the Effects Of Driver's Settingsmentioning

confidence: 99%

“…Morales et al (2015) explained how the work with forestry cranes could be speeded up using automation software. According to Morales et al (2015), time-efficiency could increase by at least three-fold if done by an autonomous control system. The reason for this claim, as shown in Table 4, has to do with the fact that machine operators -and humans in general -are not able to work quickly in multitasking problems.…”

Section: Crane Velocities Showing the Effects Of Driver's Settingsmentioning

confidence: 99%

“…Nevertheless, some initial hardware products are appearing in the market that could facilitate the development of intelligent hydraulic control. Some initial products featuring applications of this kind of hardware are the John Deere Smooth Boom Control (John Deere 2012), and Komatsu's Smart Flow (Morales et al 2015). Development of intelligent hydraulics represents a stepping stone towards automation of crane motions (La Hera and Morales 2015).…”

Section: Crane Velocities Showing the Effects Of Driver's Settingsmentioning

confidence: 99%

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

Hera

Morales²

2019

Croat. j. for. eng. (Online)

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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.

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Section: Purpose Of This Articlementioning

confidence: 99%

Section: Crane Motion Patternsmentioning

confidence: 99%

Section: Crane Velocities Showing the Effects Of Driver's Settingsmentioning

confidence: 99%

Section: Crane Velocities Showing the Effects Of Driver's Settingsmentioning

confidence: 99%

Section: Crane Velocities Showing the Effects Of Driver's Settingsmentioning

confidence: 99%

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

Hera

Morales²

2019

Croat. j. for. eng. (Online)

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Exploring the feasibility of autonomous forestry operations: Results from the first experimental unmanned machine

La Hera,

Mendoza‐Trejo,

Lindroos

et al. 2024

Journal of Field Robotics

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This article presents a study on the world's first unmanned machine designed for autonomous forestry operations. In response to the challenges associated with traditional forestry operations, we developed a platform equipped with essential hardware components necessary for performing autonomous forwarding tasks. Through the use of computer vision, autonomous navigation, and manipulator control algorithms, the machine is able to pick up logs from the ground and manoeuvre through a range of forest terrains without the need for human intervention. Our initial results demonstrate the potential for safe and efficient autonomous extraction of logs in the cut‐to‐length harvesting process. We achieved a high level of accuracy in our computer vision system, and our autonomous navigation system proved to be highly efficient. This research represents a significant milestone in the field of autonomous outdoor robotics, with far‐reaching implications for the future of forestry operations. By reducing the need for human labor, autonomous machines have the potential to increase productivity and reduce labor costs, while also minimizing the environmental impact of timber harvesting. The success of our study highlights the potential for further development and optimization of autonomous machines in the forestry industry.

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