A Generalized Vision-based Stiffness Controller for Robot Manipulators with Bounded Inputs

Generally, stiffness and impedance control schemes require knowledge of the location of any object with which a robot interacts within its workspace; therefore, the integration of a computer vision system within the control loop allows us to know the location of the robot end effector and the object (target) simultaneously. In this paper, a generalized and saturating vision-based stiffness controller with adaptive gravity compensation is presented. The proposed control algorithm is designed to regulate robot-environment interaction in task-space, where the contact force is modeled as a vector of generalized bounded spring-like forces. In order to control nonredundant robots, the proposed controller has a nonlinear proportional-derivative structure with static model-based compensation of gravitational forces, as it includes a regressor-based adaptive term. To support the proposal, the Lyapunov stability analysis of the closed-loop equilibrium vector is presented. Finally, the suitable performance of the proposed scheme was verified by numerical simulations and experimental tests.