The wrist flexibility of an industrial manipulator and flexible objects in standard assembly tasks, such as a peg-in-hole or beam-in-slot, poses challenges. Residual vibrations from high-speed motion increase assembly time and negatively affect overall productivity. This study aims to propose an innovative vision-based control strategy to manage flexibility and reduce assembly time.
The control strategy suppresses robot vibrations without altering the dynamics model or the internal controller. Its stability and bounds are determined, and its effectiveness is validated through peg-in-hole experiments. A low-frame-rate camera serves as a vision sensor for low-cost automation. Image processing and Python are used to detect vibrations in the object.
A pure wrist actuation control strategy is used to suppress vibration, reducing the vibration amplitude by approximately 96% within 3 s, leading to an approximately 96.38% reduction in stability time during the peg-in-hole assembly task. In addition, the strategy conserves approximately 65.14% of energy and reduces carbon emissions in repetitive industrial operations.
This method reduces vibrations and can be used in industrial robots during the assembly of objects with stepped cross sections. Suitable for peg-in-hole assembly, spot welding on stationary jigs or PCB inspection tasks.
This work examines both flexibility and stiffness using a low-cost vision camera with a pure wrist control strategy. Energy consumption is reduced by an average of approximately 65.14% due to reduced arm movement compared with other control methods, without affecting the robot’s internal controller.
