Assembly of low-stiffness parts through admittance control with adaptive stiffness

Existing studies on robot assembly mostly focus on high-stiffness metal parts, which are unsuitable for assembling low-stiffness parts. Additionally, when assembly is achieved by the conventional admittance control method, the number of parameters to be adjusted is significant, and unstable contact force may occur. This study proposes two algorithms, namely admittance control with variable stiffness (ACVS) and admittance control with adaptive stiffness (ACAS), which are suitable for robotic assembly of low-stiffness parts. The proposed ACVS is achieved without a position trajectory, which reduces the number of parameters that need to be adjusted by the user. Furthermore, overshoot is suppressed by calculating the stiffness parameter in real time. The ACAS scheme adjusts the stiffness value in ACVS through a multilayer feedforward neural network; therefore, its overshoot is lower than that of ACVS, and the contact force stability is enhanced. The ACAS scheme thus characterized by a slight overshoot and stable contact force enables stable assembly of low-stiffness parts via the maximum force generated by the robot.