A control framework is proposed that autonomously modulates a dexterous grasp synergy to proactively prevent grasped object slip. The proposed controller offers a practical means of preventing slip in uncertain environments, such as upper-limb prosthetics applications. The controller was evaluated with slip prevention experiments in which a dexterous manipulator grasped four separate objects and was subsequently excited by a robotic arm. Results showed that the proposed strategy significantly reduced grasped object slip.

An adaptive synergy controller is presented which allows a dexterous artificial hand to unscrew and screw an object using facial expressions derived from electromyogram (EMG) signals. In preliminary experiments, the finger joint motions of nine human test subjects were recorded as they unscrewed a bottle cap in multiple orientations of their hands with respect to the object. These data were used to develop a set of adaptive sinusoidal joint synergies to approximate the orientation-dependent human motions, which were then implemented on a dexterous robotic manipulator via the proposed adaptive synergy controller. The controller is driven through a noninvasive interface which allows a single input to drive the bioinspired human motions using facial expressions. The adaptive synergy controller was evaluated by four able-bodied subjects who were able to unscrew and screw an instrumented object using the artificial hand in two orientations with a 100% success rate.

Sliding mode and integral sliding mode slip prevention controllers are implemented for a prosthetic hand and compared to a proportional derivative shear force feedback slip prevention controller. The integral sliding mode slip prevention controller provides a robust design framework for slip prevention while addressing the issue of reducing the amount of deformation imparted to the grasped object to stop slip. Averaged results from bench top experiments show that the integral sliding mode slip prevention controller produces the least amount of deformation to the grasped object while simultaneously preventing the object from being dropped.

Three different object slip prevention controllers are incorporated into a force feedback controller for a prosthetic hand. Proportional and proportional-derivative shear force feedback controllers are explored in addition to an adaptive slip prevention algorithm to update the controllerpsilas estimate on the coefficient of friction as slips occur. Bench top experiments reveal a statistically significant improvement of the adaptive slip prevention controller over the others. Results from eight human test subjects indicate that all three methods of slip prevention significantly improve upon force control.