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2020 | OriginalPaper | Chapter

Payload Estimation in Excavators Using a Machine Learning Based Parameter Identification Method

Authors: Ashwin Walawalkar, Steffen Heep, Martin Frank, Christian Schindler

Published in: Advances in Dynamics of Vehicles on Roads and Tracks

Publisher: Springer International Publishing

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Abstract

This paper documents a part aspect of a broader work, where the goal is to develop a self-sufficient method for continuous and dynamic payload estimation for hydraulic excavators. Self-sufficiency here implies that the required unknowns are either measurable or can be identified using simple sensors. Results from field tests have showed that the approach for identification in its basic form is easy to implement which comes at the cost of diminished estimation accuracy, especially concerning the moment of inertia and friction behavior. Specifically, this paper covers the work done regarding application of machine learning methods to improve the accuracy and reliability of the identification approach, thereby consequently improving the accuracy of the payload estimation.
Footnotes
1
The bucket is actually rigidly attached to the quickfit, which in turn is connected with a revolute joint to the end of the Link 3 (Arm). In the subsequent text, Link 4 refers to the quickfit, especially as far as parameter estimation is concerned.
 
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Metadata
Title
Payload Estimation in Excavators Using a Machine Learning Based Parameter Identification Method
Authors
Ashwin Walawalkar
Steffen Heep
Martin Frank
Christian Schindler
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-38077-9_190

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