Experimental study on image-based position-force adaptive visual servoing for constrained robots under Jacobian and dynamic friction uncertainties

In this paper an image-based control is introduced to produce simultaneous convergence of the constrained visual position and contact force between the end-effector and the constraint surface. Camera, robot and Jacobian parameters are considered uncertain. This new approach is based on a new formulation of the orthogonalization principle used in force control, coined here visual orthogonalization principle. This allows, under the framework of passivity, to yield a synergetic scheme that fuses camera, encoder and force sensor signals. However, when complex friction arises, visual servoing suffers to drive the robot to the desired trajectories, in particular in slow motion and velocity reversals, which are typical motions in this sort of control schemes. Moreover, dynamic friction is usually neglected in motion control and it is not the exception in visual servoing literature. In our case, we explore an interesting result to compensate for uncertain dynamic friction. In order to prove the theory described in this article, the real-time OS, Linux-RTAI, is used to obtain experimental results of this controller on a direct-drive robot manipulator equipped with six axis JR3 force sensor. Results suggest its excellent performance.