External Sensor-less Fingertip Force/Position Estimation Framework for a Linkage-based Under-actuated Hand with Self-locking Mechanism

Precision grasping is an important skill for robotic hands to master so that they can be utilized in various manipulation tasks. To control the robotic hand precisely, modeling the kinematics and statics behavior of the robotic hand is one of the active areas of robotic research. While becoming popular because of their self-adaptability in robust power grasping, linkage-based under-actuated hands are difficult to model analytically for precision fingertip grasping, due to the stochastic and nonlinear dynamical behavior caused by the use of passive mechanisms inside each finger. In this paper, we proposed a fingertip force/position estimation framework, which detects in real-time using internal sensors data whether the passive locking mechanism is in action or not and uses the kinematics and statics models with gravity compensation in each case to compute the estimation. Using the proposed framework, an example of a precision grasping task is carried out to evaluate its reliability and show its potential to be used for future dexterous manipulation tasks.