Force Control in Flexible Object Grasping Based on Position Predictor A novel manipulation method for grasping flexible objects with a simple rigid robot hand is proposed in this study. Based on a position predictor, it mainly focuses on force control during the object grasping. A desired force for grasping is given at first, then during the process of grasping it is converted to a predicted displacement. An online linear approximator for solving the predicted displacement iteratively is proposed. Here Model Predictive Control (MPC) is adopted to achieve accurate position control. Meanwhile a flexible model is designed for the simulation and verification. This method is verified in a flexible object contact situation and the result indicates that it can control the force precisely in flexible object grasping and achieve better performance compared with admittance control.

Realtime Dynamic Grasping Force Optimization of Multifingered Dextrous Hand * A technique for realtime dynamic grasping force optimization is presented in this article. The force optimization is divided into online and offline two parts. The improved linear constrained gradient flow algorithm reduces the dimension of the description matrix based on the traditional gradient flow, and the nonnegative linear combination method is introduced to optimize the initial force of the algorithm. Relatively optimal initial grasping force and dimensionality reduction gradient flow algorithm improve the calculating efficiency online. The corresponding illustrative examples are made to verify that the proposed method to obtain both optimal grasping force and faster convergence rate in the dynamic grasp process is feasible.