A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2013; you can also visit the original URL.
The file type is application/pdf
.
Grasping-Force Optimization for Multifingered Robotic Hands Using a Recurrent Neural Network
2004
IEEE Transactions on Robotics and Automation
Grasping-force optimization of multifingered robotic hands can be formulated as a problem for minimizing an objective function subject to form-closure constraints and balance constraints of external force. This paper presents a novel recurrent neural network for real-time dextrous hand-grasping force optimization. The proposed neural network is shown to be globally convergent to the optimal grasping force. Compared with existing approaches to grasping-force optimization, the proposed
doi:10.1109/tra.2004.824946
fatcat:uh6a45fyyjb3nkolghde3fqrui