TRAJECTORY MONITORING OF INDUSTRIAL ROBOT MOTION USING A LASER TRACKER: A CASE STUDY
DOI:
https://doi.org/10.29227/IM-2026-01-01-046Keywords:
laser tracking, industrial metrology, trajectory measurement, accuracy and repeatability, dynamic measurement, robotic arm, motion validationAbstract
With the increasing level of automation in manufacturing processes, the demand for accurate validation of industrial robot motion continues to grow. This paper focuses on the application of the Leica AT403 laser tracker for monitoring the basic trajectory of the IRB 120 robotic arm. The aim was to verify the accuracy and repeatability of the robot's motion during predefined tasks in real time. For measurement, a 0.5" retroreflective spherical target (RRR) was mounted on the IRB 120 robot arm, and its motion was tracked using the AT403 laser tracker within the production facilities of ELAP. The measurement was conducted in dynamic mode using the Continuous Distance function, and the data were analyzed using PolyWorks Inspector software. The robot performed a series of predefined trajectories – a straight line, a rectangular path, and a spatial curve. The measured data demonstrated that the robot's trajectories could be recorded with deviations not exceeding 1 mm. High repeatability of motion was confirmed, although minor geometric deviations were observed at the end points of more complex trajectories. The results confirmed the potential of the system for accurate motion validation without the need for external calibration. The combination of laser measurement and robotic motion proves to be an effective tool for industrial metrology, diagnostics, and trajectory validation. This approach may be utilized to ensure production quality, support predictive maintenance, optimize manufacturing cycles, and assist in the training and calibration of robotic systems without disrupting operations. The key advantage of the proposed method lies in the integration of high-precision, non-contact measurement technology with dynamic testing of robotic trajectories in real time. This enables not only verification but also prediction of robot performance under changing conditions. In the future, the system could be extended by integrating AI for autonomous deviation assessment or connected to a digital twin (a virtual model of the physical system) for simulation and optimization of production processes in real time.
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