SCARA robots (Selective Compliance Assembly Robot Arm) are perfect for pick-and-place jobs due to their four axes and parallel-axis joint configuration. It allows for fast and accurate horizontal motions. They have a base, two parallel arms, and a vertical Z-axis for lifting and positioning. SCARA robots place components on PCBs with sub-millimeter precision in electronics. They insert screws and nuts repetitively in car production for quality and speed.
Similarly, SCARA robots precisely handle pharmaceutical bottles to lower contamination. Moreover, they are faster and more accurate than Cartesian or delta robots for horizontal motions for fast-cycle applications. For example, they can do 120 pick-and-place cycles per minute with 0.01 mm repeatability. Meanwhile, SCARA robot calibration maintains task accuracy. That matters when even little errors could decrease production.
The Need for High-Precision Calibration
SCARA robot calibration helps attain high precision in robotic operations to impact efficiency. Poor calibration results in misalignment, repeatability errors, and inaccurate positioning, which can degrade performance. For example, a misalignment of 0.1 mm in a SCARA robot could cause massive deviations over multiple operations to affect the assembly of micro-components with tight tolerances.
High-precision calibration can improve performance with lower cycle times and the need for rework. It gives product quality, upholds consistent output, and meets manufacturing standards.
Calibration Techniques for SCARA Robots
Manual Calibration Methods
Manual calibration of SCARA robots includes precise measurement and adjustment of the robot's kinematic parameters. Technicians use high-accuracy dial indicators, laser trackers, and precision gauges. First, the robot's end-effector is moved to predefined positions, and any discrepancies from the expected positions are recorded. The errors are used to adjust joint offsets, link lengths, and gear ratios.
For instance, a common practice is to measure the positional deviation at multiple points within the robot's workspace for an error map. Then, the error values are employed to iteratively fine-tune the control software. It demands knowledge of the robot's kinematic model and can be time-consuming, but it results in accurate SCARA robot calibration.
Automated Calibration Systems
Automated calibration systems utilize computer vision, laser interferometry, and machine learning. They can accomplish calibration tasks with high precision and repeatability for lower interruption. For example, a vision-based system might use a high-resolution camera to track the robot's end-effector movements. It compares them against a digital twin of the robot's ideal kinematics.
Errors are corrected in real-time while updating the robot's control parameters. Plus, laser interferometers can measure minute deviations in the robot's movement for data that can be fed into an ML algorithm to correct positional errors. Automated systems boost SCARA robot calibration accuracy and steady performance across units for high-volume manufacturing environments.
High-Precision Calibration Methods
Laser-based Calibration Systems
Laser-based systems for SCARA robot calibration project a laser beam onto a reference target and measure the deviations between the projected and actual positions of the robot's end effector. They utilize laser interferometry to detect minute positional changes with sub-micron accuracy.
For instance, a common setup might involve a HeNe laser, which provides a wavelength of 633 nm for high resolution. The advantage of this method is its precision. Laser systems can reach accuracy within 1 micron. However, limitations include sensitivity to temperature variations and vibrations, which can introduce errors. Laser calibration systems can also be costly and require alignment and upkeep.
Vision-based Calibration Techniques
Vision-based calibration techniques integrate camera systems with SCARA robots for positioning accuracy through visual feedback. They may use high-resolution cameras with algorithms to track fiducial markers or features on the robot or workpiece.
With convolutional neural networks, the system can increase calibration accuracy by learning and compensating for systematic errors over time. For example, real-time visual feedback can correct deviations down to 50 microns for task precision. Nevertheless, integrating these systems requires calibrating the camera setup itself and image processing capabilities to handle erratic lighting conditions and occlusions.
Force and Torque-Sensing for Calibration
Force and torque sensing in SCARA robot calibration is key to applications requiring high precision in dynamic assembly or material handling tasks. The sensors measure the forces and torques on the robot's end effector for the control system to adjust the robot's movements for accuracy. E.g., when the robot must apply a constant force while assembling gentle parts, force sensors with a resolution of 0.01 N can give precise control.
The benefit is the ability to compensate for unexpected disturbances that affect the robot's adaptability. However, implementing such systems needs control algorithms and sensor data integration into the robot's feedback loop, which can be computationally intensive.
Future Trends in SCARA Robot Calibration
SCARA robot calibration is improving with better sensor integration and high-resolution encoders while lowering error margins to sub-micron levels. Besides, predictive calibration analyzes performance data to forecast and fix errors for less unavailability.
Neural networks can forecast misalignments using previous data and modify real-time settings. In addition, robots with integrated sensors are monitoring and recalibrating without human interaction. Digital twin technology allows virtual calibration. It replicates real-world circumstances to fine-tune robot characteristics before deployment for greater efficiency and slashed maintenance costs.
Want to see what we have incorporated into our SCARA robots for calibration? Click here.
