Manual insertion of glass into car bodies is an intensive and imprecise job, with the opportunity for body damage and glass breakage leading to significant safety risks to assembly operators.
Alternatively, robotic insertion methods are complicated because the car body location is not precise, and therefore requires vision guidance to transform robot end effectors to compensate for body location variation and to optimize the position of the glass.
The system integrator Bluewrist is a long-term partner of LMI, specialized in the development and marketing of robotics and machine vision solutions, including 3D inspection, robot guidance, bin picking, 3D scanning and robot calibration.
The Solution
Bluewrist mounted 4 Gocator 2300 series 3D smart sensors on the robot end effector of their insertion system in order to monitor location of 4 critical points on the windshield aperture. Technicians used Gocator’s built-in Edge Tool to determine the 3D location of critical points on the aperture.
Gocator 3D measurements were then communicated to an external PC equipped with Bluewrist EzRG Advanced Robot Guidance Software, which calculates transformation data in 6 degrees of freedom and then sends its calculations to the robot controller. EzRG provides a wide range of robot guidance and user-frame calculation strategies, including Best-fit of Measurement, 3-2-1 Fixturing, and a User-Frame Formulas Interpreter. The calculation takes less than 0.5
seconds and the guidance accuracy can achieve up to 0.2mm.
The following is the sequence of the windshield insertion operation:
Najah Ayadi, President of Bluewrist, declared that: “The Gocator’s built-in smart measurement and exposure control delivers a 3D solution at incredible value.’’
Gocator easily mounts on robot end effectors for complex 6DOF guidance for assembly
Long term partnerships lead to solutions for new applications
The Bluewrist Windshield Insertion System was successfully demonstrated at AMTS 2015, providing a strong example of how LMI and Bluewrist are working together to create more robust robotic guidance machine vision systems.