Practical object-grasp estimation without visual or tactile information for heavy-duty work machines

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper proposes a practical framework to estimate whether or not a grapple installed in demolition machines is in a grasp state. Object grasp is a highly difficult task that requires safe and precise operations, so identifying a grasp or non-grasp state is important for assisting an operator. These types of outdoor machines lack visual and tactile sensors, so the proposed framework adopts practically available lever operation and cylinder pressure sensors. The grasp is formed by a grasp motion, which is operations to make the grapple pinch an object, and the grasp state, where the grapple holds the object in any manipulator movements. Thus, the framework determi-nately confirms the grasp motion through the requisite conditions defined by using sequential changes of binarized operation and pressure data for the grapple and the manipulator, and stochastically confirms the grasp state through the enhancement conditions defined by using force and movement vectors including vertical downward force, movement in the longer direction, and horizontal reciprocating movement. The results of experiments conducted to transport objects using an instrumented hydraulic arm indicated that the proposed framework is effective for identifying grasp/non-grasp with high accuracy, independently of various operators and environments.

Original languageEnglish
Title of host publicationIROS 2013
Subtitle of host publicationNew Horizon, Conference Digest - 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
Pages3210-3215
Number of pages6
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013 - Tokyo, Japan
Duration: 2013 Nov 32013 Nov 8

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Other

Other2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013
CountryJapan
CityTokyo
Period13/11/313/11/8

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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    Kamezaki, M., Iwata, H., & Sugano, S. (2013). Practical object-grasp estimation without visual or tactile information for heavy-duty work machines. In IROS 2013: New Horizon, Conference Digest - 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 3210-3215). [6696812] (IEEE International Conference on Intelligent Robots and Systems). https://doi.org/10.1109/IROS.2013.6696812