Abstract
This paper proposes a method for modeling and planning the grasping configuration of a robotic hand with underactuated finger mechanisms. The proposed modeling algorithm is based on analysis and mimicking of human grasping experience. Results of the analysis is preprocessed and stored in a database. The grasp configuration planning algorithm can be used within a real time online grasp control as based on artificial neural networks. Namely, shapes and sizes of task objects are described by taxonomy data, which are used to generate grasp configurations. Then, a robot hand grasp control system is designed as based on the proposed grasp planning with close-loop position and force feedback. Simulations and experiments are carried out to show the basic features of the proposed formulation for identifying the grasp configurations while dealing with target objects of different shapes and sizes. It is hoped that the well-trained underactuated robot hand can solve most of grasping tasks in our life. The research approach is aimed to research low-cost easy-operation solution for feasible and practical implementation.
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.
