Title
Gesture recognition based teleoperation framework of robotic fish
Abstract
Robots attract strong interest from human beings, and ordinary people seriously expect to acquire intuitive understanding from the process of interacting with robots. In this paper, a teleoperation framework based on gesture recognition was developed and the recognized human gestures were mapped to corresponding swimming behaviors of underwater robotic fish. By this means, the robotic fish can be remotely controlled by hand gestures. Most significantly, the teleoperation framework offers the opportunity for onlookers to directly interact with the robotic fish, and the intuitive experience of onlookers about human-robot interaction can be augmented. Compared with traditional control structures of underwater robotic fish systems, the presented teleoperation framework can be built quickly, the influence of light condition can be eliminated entirely, and the onlookers can interact with robotic fish directly rather than need to learn about the system architecture and control strategy. Several tests were taken in a water pool to verify the performance of the presented teleoperation framework. The experimental results showed that the developed teleoperation framework is suitable for remote controlling underwater robotic fish, and the teleoperation framework can be widely applied to other application scenarios. The experiment setup was exhibited in IROS2015, Hamburg, the described teleoperation framework greatly attracted onlooker's interest.
Year
DOI
Venue
2016
10.1109/ROBIO.2016.7866311
2016 IEEE International Conference on Robotics and Biomimetics (ROBIO)
Keywords
Field
DocType
gesture recognition based teleoperation framework,robotic fish,swimming behaviors,underwater robotic fish,human-robot interaction,light condition,water pool,IROS2015,Hamburg
Teleoperation,Computer vision,Gesture,Gesture recognition,Robot kinematics,Artificial intelligence,Engineering,Systems architecture,Robot,Servomotor,Underwater
Conference
ISBN
Citations 
PageRank 
978-1-5090-4365-1
0
0.34
References 
Authors
7
7
Name
Order
Citations
PageRank
Mi Jinpeng100.68
Yu Sun286.32
Yu Wang311.03
Deng Zhen400.34
Liang Li527714.18
Zhang Jianwei643.27
Xie Guanming700.34