This paper discusses the grasp force sensing capabilities of the fingernail imaging method integrated with a visual servo-ing robotic system. The effectiveness of the fingernail imaging method has been demonstrated on the previous works in the prediction of 3-D fingertip forces. In this study, the fingernail imaging method has been modified to be used in constrained grasping studies. Moreover, the technique can be extended to be applied to the unconstrained grasping study as well. Visual servoing has been utilized in this paper to solve the issue of keeping fingernail images in the field of view of the camera during unconstrained grasping motions. The experimental results show the effectiveness of applying visual servoing for use with the fingernail imaging method to be used in grasping studies. Experimental studies were performed on 2 human subjects and the mean value of RMS errors for predicted normal forces during grasping has been found as 0.57 N. (5.7% for the range of 0-10 N).
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