On the Accuracy of Articulated Robots

Abstract

Traditionally, robots have primarily been used in tasks with minimalor no contact with the environment, such as material handling and painting.The articulated robot, with its flexibility, adaptability, affordability, andlarge workspace, is well-suited for a wide range of contact applications requiringcontinuous interaction with the environment. However, the inherent lowstructural stiffness of articulated robots can lead to significant deformationunder external loads, which, in turn, affects the accuracy of the end-effector’spositioning. Therefore, improving accuracy is crucial for the widespread adoptionof robotic systems in high-precision contact applications.This research investigates the impact of load and motion on the positioningaccuracy of articulated robots in contact applications. The analysisperformed on quasi-static deflections reveals that traditional static calibrationmethods can underestimate actual positioning errors by neglecting thecombined effects of load and motion.To address this, a model-based quasi-static compliance calibration methodis proposed. This method leverages a joint stiffness model to estimate andcompensate for robot deformation. Experimental results demonstrate significantaccuracy improvements, with positioning error reductions ranging from60% to 90%, depending on the robot, application, workspace, and load conditions.To enhance the practical applicability of the method, a balance amongaccuracy, computational efficiency, and ease of implementation is prioritized.The quasi-static approach results in a suitable compromise between accuracylevel and resource requirements.To further contribute to mainstream calibration accuracy improvements inindustrial settings, this work demonstrates the feasibility of transferring jointstiffness parameters, identified through quasi-static analysis, among identicalrobots sharing similar tasks, loads, and operational spaces. This transferbasedcompensation approach was compared to conventional compensationapproaches to assess its effectiveness in minimizing load-induced errors.Finally, to effectively evaluate robot performance in contact applications,a comprehensive set of testing conditions is proposed. These conditions considerfactors such as load, velocity, directionality, and workspace coverage,which can deliver a more rigorous assessment of robot capabilities.Future research directions include investigating the interplay betweenquasi-static and dynamic effects, exploring advanced modeling techniques thatcombine physics-based and data-driven approaches to address residual errors,and developing robust performance evaluation procedures for complex roboticsystems in emerging contact applications.

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