Performance enhancement of two-camera robotic system using adaptive gain approach

This paper aims to improve the performance of a two-camera robotic feedback system designed for automatic pick and place application by modifying its velocity profile during switching of control.

Cooperation of global and local vision sensors ensures visibility of the target for a two-camera robotic system. The master camera, monitoring the workspace, guides the robot such that image-based visual servoing (IBVS) by the eye-in-hand camera transcends its inherent shortcomings. A hybrid control law steers the robot until the system switches to IBVS in a region proven for its asymptotic stability and convergence through a qualitative overview of the scheme. Complementary gain factors can ensure a smooth transition in velocity during switching considering the versatility and range of the workspace.

The proposed strategy is verified through simulation studies and implemented on a 6-DOF industrial robot ABB IRB 1200 to validate the practicality of adaptive gain approach while switching in a hybrid visual feedback system. This approach can be extended to any control problem with uneven switching surfaces or coarse/fine controllers which are subjected to discrete time events.

In complex workspace where robots operate in parallel with other robots/humans and share workspaces, the supervisory control scheme ensures convergence. This study proves that hybrid control laws are more effective than conventional approaches in unstructured environments and visibility constraints can be overcome by the integration of multiple vision sensors.

The supervisory control is designed to combine the visual feedback data from eye-in-hand and eye-to-hand sensors. A gain adaptive approach smoothens the velocity characteristics of the end-effector while switching the control from master camera to the end-effector camera.