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. © 2021, ICROS, KIEE and Springer.

Adaptive control; bounded inputs; robot manipulator; stability; stiffness; vision Computer control systems; End effectors; Manipulators; Robot applications; Stiffness; Vector spaces; Computer vision system; Experimental test; Gravitational forces; Gravity compensation; Lyapunov stability analysis; Proportional derivatives; Robot end effector; Robot-environment interaction; Controllers

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