Robot grasp synthesis algorithms: A survey

This article presents a survey of the existing computational algorithms meant for achieving four important properties in autonomous multifingered robotic hands. The four properties are: dexterity equilibrium stability and dynamic behavior The multifingered robotic hands must be controlled so as to possess these properties and hence be able to autonomously perform complex tasks in a way similar to human hands. Existing algorithms to achieve dexterity primarily involve solving an unconstrained linear programming problem where an objective function can be chosen to represent one ol more of the currently known dexterity measures. Algorithms to achieve equilibrium also constitute solving a linear program ming problem wherein the positivity, friction, and joint torque constraints of all fingers are accounted for while optimizing the infernal grasping forces. Stability algorithms aim at achieving positive definite grasp impedance matrices by solving for the required fingertip impedances. This problem reduces to a nonlinear programming problem. Dynamic behavior algorithms determine fingertip impedances, which, when achieved, lead to a desired dynamic behavior This problem, too, becomes a linear programming problem. If a robotic hand has to acquire any or all of these properties, the corresponding algorithms should become integral parts of the hand control system. These algorithms are collectively referred to in this article as robot grasp synthesis algorithms.