Random time delay may cause instability in the internet based teleoperation system. Transparency and intuitiveness are also very important for operator to control the system to accurately perform the desired action, especially for the gripper teleoperation system. This paper presents a new grip force control method of gripper teleoperation system with haptic feedback. The system employs the SEMG signal as the control parameter in order to enhance the intuitive control experience for operator. In order to eliminate the impacts on the system stability caused by random time delay, a non-time based teleoperation method is applied to the control process. Besides, neural network and designed fuzzy logic controller is also utilized to improve this control method. The effectiveness of the proposed method is demonstrated by experiment results.
Posture recognition is a very important Human-Robot Interaction (HRI) way. To segment effective posture from an image, we propose an improved region grow algorithm which combining with the Single Gauss Color Model. The experiment shows that the improved region grow algorithm can get the complete and accurate posture than traditional Single Gauss Model and region grow algorithm, and it can eliminate the similar region from the background at the same time. In the posture recognition part, and in order to improve the recognition rate, we propose a CNN ensemble classifier, and in order to reduce the misjudgments during a continuous gesture control, a vote filter is proposed and applied to the sequence of recognition results. Comparing with CNN classifier, the CNN ensemble classifier we proposed can yield a 96.27% recognition rate, which is better than that of CNN classifier, and the proposed vote filter can improve the recognition result and reduce the misjudgments during the consecutive gesture switch.
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