The overwhelming majority of industrial robots operate on the principles of Model-based Control. This methodology, while effective, inherently introduces errors because any model is an abstraction of reality, simplifying complex dynamics and physical interactions that can vary slightly in the real world. These discrepancies necessitate a rigorous process of calibration to ensure the robots perform tasks with the highest possible accuracy. Calibration of industrial robots is not a singular action but a comprehensive, ongoing process that includes modeling, precise measurement, identification of the robot's actual parameters versus its theoretical models, and the meticulous implementation of error compensation strategies.
Understanding the genesis of errors in robotic systems is foundational to their calibration. Errors in industrial robots stem from a multitude of sources, broadly categorized into kinematic and dynamic factors. Kinematic-related errors include those arising from the physical construction and mechanical operation of the robot, such as machining inaccuracies, mechanical tolerances or assembly errors, zero-point deviations, backlash in gear systems, and inaccuracies in gear ratios and calibration processes. On the other hand, dynamic-related errors are associated with how the robot moves and responds to forces, encompassing variations in mass or center of gravity, inertia tensor discrepancies, frictional forces, and the flexibility of joints and connecting links.
Practical experience has illuminated that uncalibrated robots can exhibit significant inaccuracies, with base errors ranging between 15 to 30mm, errors at the Tool Center Point (TCP) of 5 to 10mm, and overall systemic errors also within the 5 to 10mm range. Through diligent calibration, these error margins can be drastically reduced, enhancing the robot's precision and reliability.
The implications of neglecting robot calibration are profound. Uncalibrated robots are incapable of sharing programs effectively due to their inherent inaccuracies, resulting in low precision and instability in their operations. Conversely, a well-calibrated robot can significantly improve its adaptability and performance in dynamic environments, showcasing a heightened ability to manage uncertainties.
One of the primary drivers behind the discrepancy between a robot's designed operational parameters and its real-world performance is the inherent difference between theoretical models and actual parameters. Calibration, therefore, becomes a crucial step in bridging this gap, often enhancing the robot's precision by several orders of magnitude.
Moreover, the development and refinement of industrial robots demand access to accurate, real-world data to evaluate both static and dynamic characteristics comprehensively. This necessitates a robust calibration system capable of capturing precise data to inform adjustments and improvements. Thus, calibration is not just a means to correct errors but a critical component of the iterative process of robot design, testing, and deployment, ensuring that industrial robots can achieve and maintain the high levels of accuracy and reliability required for their complex tasks.
