Abstract
Grasp recognition is a part of Programming-by- Demonstration (PbD) for a five-fingered robotic hand. The robot receives instructions from a human operator to perform different grasps to be used for a robot task. For this purpose the finger joint angle trajectories are recorded by a dataglove and modeled by fuzzy clustering and Takagi-Sugeno modeling. The resulting grasp models use time as input and the joint angles as outputs. Given a test grasp by the human operator the robot control system recognizes the grasp and generates the type of grasp shown before. Three methods of grasp recognition are presented and compared with each other. In the first method a test grasp is compared with model grasps using the difference between the model outputs. In the second method qualitative fuzzy models are used for recognition and classification. The third method uses Hidden-Markov-Models (HMM) for recognition.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
