Geometrical calibration of a 6-axis robotic arm for high accuracy manufacturing task

Lattanzi, Luca; Cristalli, Cristina; Massa, Daniele; Boria, Sébastien; Lépine, P; Pellicciari, Marcello

doi:10.1007/s00170-020-06179-9

Cited by 31 publications

(11 citation statements)

References 27 publications

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“…Even though the problem of robot calibration is widely explored, the focus is generally on the deployment of external measurement devices (for open loop calibration [ 9 , 28 ]) or mechanical fixtures (for closed loop calibration [ 35 , 36 ]). In the approach presented in this paper, a model-based fast-kinematic re-calibration is devised whereby we use the factory-calibrated controller as an ‘oracle’ for our fast-recalibration approach.This allows for the extraction of calibrated intrinsic parameters (e.g., link lengths) otherwise not directly available from the ‘oracle’, for use in ad-hoc control strategies.…”

Section: Existing Methodologies For Kinematic Calibrationmentioning

confidence: 99%

“…The non-geometric factors contributing to accuracy errors consist of structural deformations such as backlash [ 8 ] and clearance in the transmission system as well as link flexibility, joint flexibility, slip-stick phenomena and thermal expansion [ 5 ]. However, these errors are considered to be much smaller compared to those originating from geometric factors [ 7 , 9 ]. Therefore, having a reliable method for calibrating robots is essential to ensure position accuracy at all times.…”

Section: Introductionmentioning

confidence: 99%

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Fast Kinematic Re-Calibration for Industrial Robot Arms

Kana

Gurnani

Ramanathan

et al. 2022

Sensors

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Accurate kinematic modelling is pivotal in the safe and reliable execution of both contact and non-contact robotic applications. The kinematic models provided by robot manufacturers are valid only under ideal conditions and it is necessary to account for the manufacturing errors, particularly the joint offsets introduced during the assembling stages, which is identified as the underlying problem for position inaccuracy in more than 90% of the situations. This work was motivated by a very practical need, namely the discrepancy in terms of end-effector kinematics as computed by factory-calibrated internal controller and the nominal kinematic model as per robot datasheet. Even though the problem of robot calibration is not new, the focus is generally on the deployment of external measurement devices (for open loop calibration) or mechanical fixtures (for closed loop calibration). On the other hand, we use the factory-calibrated controller as an ‘oracle’ for our fast-recalibration approach. This allows extracting calibrated intrinsic parameters (e.g., link lengths) otherwise not directly available from the ‘oracle’, for use in ad-hoc control strategies. In this process, we minimize the kinematic mismatch between the ideal and the factory-calibrated robot models for a Kinova Gen3 ultra-lightweight robot by compensating for the joint zero position error and the possible variations in the link lengths. Experimental analysis has been presented to validate the proposed method, followed by the error comparison between the calibrated and un-calibrated models over training and test sets.

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Section: Existing Methodologies For Kinematic Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast Kinematic Re-Calibration for Industrial Robot Arms

Kana

Gurnani

Ramanathan

et al. 2022

Sensors

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“…Different from the calibration methods that require prior calibration of tool and robot base (Guozhi Li et al , 2019; Jiang et al , 2021; Nubiola and Bonev, 2013; Lattanzi et al , 2020), a comprehensive calibration method with a laser tracker is adopted, which can calibrate the basic coordinate system, the kinematic parameters, the flexibility parameters and the tool coordinate system of the drilling system simultaneously.…”

Section: Comprehensive Calibrationmentioning

confidence: 99%

Calibration for a robotic drilling system with secondary encoders based on a novel enhanced rigid-flexible coupling model

Yun-fei

Zhang

Huang

et al. 2022

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Purpose This paper aims to propose a novel model and calibration method to improve the absolute positioning accuracy of a robotic drilling system with secondary encoders and additional axis. Design/methodology/approach The enhanced rigid-flexible coupling model is developed by considering both kinematic parameters and link flexibility. The kinematic errors of the robot and the additional axis are considered with a model containing 27 parameters. The elastic deformation errors of the robot under self-weight of links and end-effector are estimated with a flexible link model. For calibration, an effective comprehensive calibration method is developed by further considering the coordinate systems parameters of the drilling system and using a two-step process constrained Levenberg–Marquardt identification method. Findings Experiments are performed on the robotic drilling system that contains a KUKA KR500 R2830 industrial robot and an additional lifting axis with a laser tracker. The results show that the maximum error and mean error are reduced to 0.311 and 0.136 mm, respectively, which verify the effectiveness of the model and the calibration method. Originality/value A novel enhanced rigid-flexible coupling model and a practical comprehensive calibration method are proposed and verified. The experiments results indicate that the absolute positioning accuracy of the system in a large workspace is greatly improved, which is conducive to the application of industrial robots in the field of aerospace assembly.

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“…AI has already transformed manufacturing in many ways. For instance, we have robotic arms for robotized assembly [3], which is useful when conducting highly repetitive and high risk tasks to reduce time cost and protect humans from danger. Another case is where computer vision takes place for defect detection, especially when the flaws in products are too small for inspectors to notice with the naked eye [4] [5].…”

Section: Introduction a Artificial Intelligence In Manufacturingmentioning

confidence: 99%

Multi-Modal Chatbot in Intelligent Manufacturing

Chen

Chiu

et al. 2021

IEEE Access

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Artificial intelligence (AI) has been widely used in various industries. In this work, we concentrate on what AI is capable of doing in manufacturing, in the form of a chatbot. We designed a chatbot that helps users complete an assembly task that simulates those in manufacturing settings. In order to recreate this setting, we have users assemble a Meccanoid robot through multiple stages, with the help of an interactive dialogue system. Based on classifying users' intent, the chatbot is able to provide answers or instructions to the user when the user encounters problems during the assembly process. Our goal is to improve our system so that it can capture users' needs by detecting their intent and therefore provide relevant and helpful information to the user. However, in a multiple-step task, we cannot rely on intent classification with user question utterance as the only input, as user questions raised from different steps may share the same intent but require different responses. In this paper, we proposed two methods to address this problem. One is that we capture not only textual features but also visual features through the YOLO-based Masker with CNN (YMC) model. Another is the usage of an Autoencoder to encode multi-modal features for user intent classification. By incorporating visual information, we have significantly improved the chatbot's performance from the experiments conducted on different dataset.INDEX TERMS chatbot, human-robot interaction, multi-modal intent classification

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Geometrical calibration of a 6-axis robotic arm for high accuracy manufacturing task

Cited by 31 publications

References 27 publications

Fast Kinematic Re-Calibration for Industrial Robot Arms

Fast Kinematic Re-Calibration for Industrial Robot Arms

Calibration for a robotic drilling system with secondary encoders based on a novel enhanced rigid-flexible coupling model

Multi-Modal Chatbot in Intelligent Manufacturing

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