Robot-Mounted Inspection: Accuracy Requirements and Approaches
The landscape
Robot-mounted inspection systems combine a precision sensor with a positioning platform (the robot arm). The total measurement uncertainty is a function of both: sensor accuracy and robot positioning accuracy. In most configurations, the robot's positioning error is the dominant contributor to total measurement uncertainty.
- Sensor accuracy — Typical range: ±0.01–0.1 mm. Source: sensor quality and calibration. Addressable by: sensor manufacturer.
- Robot absolute accuracy — Typical range: ±0.5–1.5 mm. Source: kinematic model error. Addressable by: kinematic calibration.
- Robot path accuracy — Typical range: ±0.2–1.0 mm. Source: dynamic errors during motion. Addressable by: dynamic tracking.
- Vibration during scanning — Typical range: ±0.05–0.5 mm. Source: structural flexibility and speed. Addressable by: vibration compensation.
- Thermal drift — Typical range: ±0.1–2 mm over 8 hours. Source: temperature-dependent expansion. Addressable by: dynamic tracking.
- Total system accuracy — Typical range: combination of the above. Source: all sources compound. Addressable by: combined software approach.
Key evaluation criteria for inspection applications
- Measurement correlation: Does the robot-mounted inspection produce results that correlate with CMM measurements? The primary acceptance criterion for automated inspection is not absolute accuracy, but consistency with the gold-standard measurement method.
- Dynamic vs. static accuracy: Inspection applications require accuracy during motion (scanning), not just at static endpoints. Path accuracy and vibration behavior during the scan trajectory are as important as point-to-point positioning.
- Stability over time: Inspection campaigns can run for hours. Accuracy drift during the campaign introduces time-dependent systematic error. Solutions must maintain accuracy over the measurement duration, not just at the start.
- Sensor independence: Does the accuracy improvement method work with any sensor payload, or is it specific to a particular sensor or measurement modality? Platform-level accuracy improvement benefits any sensor mounted on the arm.
Software compensation for inspection
Three categories of software compensation address the primary robot-side error sources:
- Kinematic calibration: Corrects the arm's geometric model. Improves positional accuracy for programmed scan paths. One-time per arm (revalidated after changes).
- Vibration compensation: Reduces TCP oscillation during scanning motion. Improves measurement signal-to-noise ratio. Feedforward — no additional sensors in production.
- Dynamic joint tracking: Real-time correction for joint-level errors. Compensates for thermal drift, backlash, and compliance during operation. Maintains accuracy over extended measurement campaigns.
What this means for your product
If you are developing or integrating robot-mounted inspection systems, the robot's positioning accuracy is likely the limiting factor in your total measurement uncertainty budget. Software-based accuracy improvement can shift the accuracy bottleneck from the robot to the sensor — which is where it should be, given that the sensor is purpose-built for measurement and the robot is a general-purpose positioning platform.
