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Multimedia Tools and Applications

, Volume 69, Issue 3, pp 1111–1130 | Cite as

A hybrid haptic guidance model for tank gunners in high precision and high speed motor skill training

  • Guanyang Liu
  • Yuru Zhang
  • Keke Lu
  • Lingzhi Liu
Article
  • 222 Downloads

Abstract

Haptic-based paradigms for human motor skill training which include virtual fixture, record-play method, shared control scheme and haptic disturbance have been proposed and widely used for applications like surgery, assembly, rehabilitation, motor skill and so on. However, no haptic-based training scheme applies to all types of human motor skills that new ideas and new approaches should be explored for some special training tasks. For example, tank gunners have to be rigorously trained to be able to complete the most accurate manipulation in the shortest possible time. Accuracy and operating speed are both critical for them to grasp the skill; therefore, tank gunnery is defined as a type of high precision and high speed human motor skill. In this paper, a hybrid spring-damper model which fuses haptic fixture and record-play is presented to simultaneously train accuracy and operating speed. The training approach is suitable for novices at all levels since force feedback is decomposed into two components: one for training accuracy, the other for training speed. The virtual envelope depicting is chosen as the training task for novices to validate the effectiveness of the proposed haptic-based scheme in high precision and high speed skill training. Experimental results indicate that force feedback generated based on the hybrid model can benefit novices in fast improving performances on tank gunnery.

Keywords

Haptic High precision and high speed Haptic-based training paradigm Human motor skill training Virtual envelope depicting 

Notes

Acknowledgment

This research received support of National Science Foundation of China under grant No. 60803070.

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Guanyang Liu
    • 1
    • 2
  • Yuru Zhang
    • 1
    • 2
  • Keke Lu
    • 1
    • 2
  • Lingzhi Liu
    • 1
    • 2
  1. 1.State Key Laboratory of Virtual Reality Technology and SystemsBeihang UniversityBeijingChina
  2. 2.Robotics Institute, School of Mechanical Engineering and AutomationBeihang UniversityBeijingChina

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