Optimal path generation for excavator with neural networks based soil models

Sanghak Lee, Daehie Hong, Hyungju Park, Jangho Bae

Research output: Contribution to conferencePaper

6 Citations (Scopus)

Abstract

In order to automate the excavating process, the path of the excavator bucket tip should be optimally generated. The following four factors must be considered when the bucket path is determined: bucket volume (soil capacity in a bucket), reachability (backhoe structure limitation), time efficiency, and soil property. Among them, the soil property is hardly quantified due to the complexity of its mechanical behavior. This paper deals with a neural network model to identify the soil property. Human operator usually determines soil type by sensing its hardness given a specific path and then plans a safe and workable path. The neural network model proposed in this paper outputs the soil type with the force and trajectory inputs. The feasibility of the proposed system is proved through the experiments with a robot equipped with a force sensor.

Original languageEnglish
Pages632-637
Number of pages6
DOIs
Publication statusPublished - 2008
Event2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI - Seoul, Korea, Republic of
Duration: 2008 Aug 202008 Aug 22

Other

Other2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI
CountryKorea, Republic of
CitySeoul
Period08/8/2008/8/22

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Science Applications

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  • Cite this

    Lee, S., Hong, D., Park, H., & Bae, J. (2008). Optimal path generation for excavator with neural networks based soil models. 632-637. Paper presented at 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI, Seoul, Korea, Republic of. https://doi.org/10.1109/MFI.2008.4648015