Hybrid Adaptive Vision-Force Control Under the Bottleneck Constraint

Lijun Han, Yuyou Zhang, Hesheng Wang

Abstract: This study proposes new hybrid vision/force control schemes for interaction with the inner surface of the bottle-like mold under the bottleneck (BN) constraint. First, to guarantee the BN constraint, the transformed error with the prescribed performance function is introduced, and the corresponding kinematic formulation is derived. Then, an adaptive kinematic-based static visual servoing controller and a dynamics-based visual tracking controller are proposed with the uncalibrated camera, the unknown 3-D position of the feature point, and the unknown surface. Both controllers can complete the corresponding tasks in the image space while ensuring that the interaction force reaches the desired value without violating the BN constraint. Through the combination of the designed two controllers, we can accomplish different tasks. For the task of cleaning a bottle, first, the servo controller is employed to guide the tooltip to the specified position on the interactive surface. Then, the tracking controller is used to control the trajectory and interactive force during the motion. Finally, the stability of proposed algorithms is rigorously proved by the Lyapunov theory, and the effectiveness is tested by experiments.