An Adaptive Control Approach to Robotic Assembly with Uncertainties in Vision and Dynamics

The objective of this paper is to propose an adaptive impedance control framework to cope with uncertainties in vision and dynamics in robotic assembly tasks. The framework is composed of an adaptive controller, a vision system, and an interaction planner, which are all supervised by a finite state machine. 
In this framework, the target assembly object's pose is detected through the vision module, which is then used for the planning of the robot trajectories. The adaptive impedance control module copes with the uncertainties of the vision and the interaction planner modules in alignment of the assembly parts (a peg and a hole in this work). Unlike the classical impedance controllers, the online adaptation rule regulates the level of robot compliance in constrained directions, acting on and responding to the external forces. This enables the implementation of a flexible and adaptive Remote Center of Compliance (RCC) system, using active control. We first evaluate the performance of the proposed adaptive controller in comparison to classical impedance control. Next, the overall performance of the integrated system is evaluated in a peg-in-hole setup, with different clearances and orientation mismatches.