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We present a proof-of-concept for controlling the grasp of an anthropomorphic mechatronic prosthetic hand by using a biomimetic tactile sensor, Bayesian inference, and simple algorithms for estimation and control. The sensor takes advantage of its compliant mechanics to provide a triaxial force sensing end-effector for grasp control. By calculating normal and shear forces at the fingertip, the prosthetic hand is able to maintain perturbed objects within the force cone to prevent slip. A Kalman filter is used as a noise-robust method to calculate tangential forces. Biologically inspired algorithms and heuristics are presented that can be implemented online to support rapid, reflexive adjustments of grip.
Wettels et al. (Thu,) studied this question.
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