Impact of Data Quantity and Composition on Bucket Filling Performance for Wheel Loaders

Daniel Eriksson, Reza Ghabcheloo, Marcus Geimer

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

This paper investigates the impact of training data with different quantities and compositions on the performance and robustness of a Neural Network (NN) controller for the wheel loader bucket filling task. Collecting training data for machine learning methods with a real-world Heavy Duty Mobile Machine (HDMM) is expensive, and therefore knowing how to collect the data and in what quantities will significantly reduce the data collection effort. We collected 2000 bucket fillings of non-homogeneous material, more specifically, a blasted rock pile with a kernel size of 0-400 mm. No previous study has reported such a challenging material composition. The collected data was divided into 6 datasets with sizes of 10, 20, 50, 100, 500, and 2000 bucket fillings. We use the Dynamic Time Warp (DTW) distance, k-medoids clustering, and the silhouette score, to create diverse and dissimilar datasets. Furthermore, one additional dataset was created with 10 bucket fillings, which are as similar as possible, resulting in 7 datasets in total. The datasets were used to synthesize 7 controllers that were then evaluated with a set of experiments to compare their performance to one another and the human operator. The results showed that the controller trained on similar bucket fillings was not robust and had poor performance, as expected. The experiment also showed that all the controllers trained on diverse data were robust enough to load the blasted rock material. However, the loaded material weight was less than the human operator, where the best controller loaded 9% less material weight, but 11% faster than the human operator.

Original languageEnglish
Title of host publication2024 European Control Conference, ECC 2024
PublisherIEEE
Pages141-147
Number of pages7
ISBN (Electronic)9783907144107
DOIs
Publication statusPublished - 2024
Publication typeA4 Article in conference proceedings
Event2024 European Control Conference, ECC 2024 - Stockholm, Sweden
Duration: 25 Jun 202428 Jun 2024

Publication series

Name2024 European Control Conference, ECC 2024

Conference

Conference2024 European Control Conference, ECC 2024
Country/TerritorySweden
CityStockholm
Period25/06/2428/06/24

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Control and Optimization
  • Modelling and Simulation

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