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Virtual interaction and manipulation control of a hexacopter through hand gesture recognition from a data glove

Published online by Cambridge University Press:  11 July 2022

Haiming Huang ,
Di’en Wu ,
Zehao Liang ,
Fuchun Sun  and
Mingjie Dong Open the ORCID record for Mingjie Dong [Opens in a new window]
Haiming Huang
Affiliation:
College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
Di’en Wu
Affiliation:
College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
Zehao Liang
Affiliation:
College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
Fuchun Sun
Affiliation:
College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China Department of Computer Science and Technology, Tsinghua University, Beijing, China
Mingjie Dong*
Affiliation:
Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing, China
*
*Corresponding author. E-mail: [email protected]
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Abstract

The purpose of this study is to realize virtual interaction and manipulation control of a hexacopter based on hand gesture recognition from a designed data glove, to provide an intuitive and visual real-time simulation system for flight control algorithm verification and external control equipment testing. First, the hand gesture recognition from a designed data glove is studied, which can recognize different actions, such as mobile ready, grab, loosen, landing, take-off, and hover. Then, the design of virtual simulation system for hexacopter capture is completed, with the model design of hexacopter and manipulator, and the simulation software design with $CoppeliaSim$ . Finally, virtual simulation experiment of hexacopter grasping and virtual flight control experiment based on data glove are tested, respectively, and quantitatively described. The overall recognition rate is 84.3%, indicating that the data glove produced has the ability to recognize gestures, but its recognition performance is not superior. In gesture recognition, the recognition rate of static gestures is relatively higher than that of dynamic gestures. Among the static gestures, the hover gesture has the highest recognition rate. The average correct rate of static gestures can reach 94%. The lowest recognition rate of dynamic gestures is upward movement, and the average recognition rate of dynamic gestures is 76.1%. The research can be used to remotely operate hexacopter using a data glove in the future and improve the control performance through virtual interaction and manipulation simulation before actual application.

Keywords

virtual simulation controlhexacopterhand gesture recognitiondata gloveCoppeliaSim
Type
Research Article
Information
Robotica , Volume 40 , Issue 12 , December 2022 , pp. 4375 - 4387
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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