An Overview of Robot Calibration

., A.Y. Elatta; Gen, Li Pei; Zhi, Fan; Yu, Daoyuan; Luo, Fei

doi:10.3923/itj.2004.74.78

Cited by 199 publications

(74 citation statements)

References 15 publications

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“…Most robot manipulators are built with excellent repeatability through manufacturing and assembly but suffer from accuracy due to the inverse kinematic computation involved during control (Elatta et al, 2004 ). Other kinematic calibrations concern the spatial synchronization between the tool and the flange, or different robot systems in co-manipulation tasks (Wu et al, 2016 ).…”

Section: Methodsmentioning

confidence: 99%

Flange-Based Hand-Eye Calibration Using a 3D Camera With High Resolution, Accuracy, and Frame Rate

Wan

Song

2020

Front. Robot. AI

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Point cloud data provides three-dimensional (3D) measurement of the geometric details in the physical world, which relies heavily on the quality of the machine vision system. In this paper, we explore the potentials of a 3D scanner of high quality (15 million points per second), accuracy (up to 0.150 mm), and frame rate (up to 20 FPS) during static and dynamic measurements of the robot flange for direct hand-eye calibration and trajectory error tracking. With the availability of high-quality point cloud data, we can exploit the standardized geometric features on the robot flange for 3D measurement, which are directly accessible for hand-eye calibration problems. In the meanwhile, we tested the proposed flange-based calibration methods in a dynamic setting to capture point cloud data in a high frame rate. We found that our proposed method works robustly even in dynamic environments, enabling a versatile hand-eye calibration during motion. Furthermore, capturing high-quality point cloud data in real-time opens new doors for the use of 3D scanners, capable of detecting sensitive anomalies of refined details even in motion trajectories. Codes and sample data of this calibration method is provided at Github ( https://github.com/ancorasir/flange_handeye_calibration ).

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Section: Methodsmentioning

confidence: 99%

Flange-Based Hand-Eye Calibration Using a 3D Camera With High Resolution, Accuracy, and Frame Rate

Wan

Song

2020

Front. Robot. AI

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“…Reconstructing the arteries result from relative motion matrices presented ( 5 ), during which errors can accumulate. These sources include the in-image localization errors and robot positioning error (

mm) of which

are attributed to robot geometric errors [ 39 ]. Catheter reconstruction errors originate from the FBG sensor reflectivity and the uncertainties from precision sphere triangulation.…”

Section: Experimental Resultsmentioning

confidence: 99%

Real-Time Multi-Modal Sensing and Feedback for Catheterization in Porcine Tissue

Heunis

Šuligoj

Santos

et al. 2021

Sensors

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Objective: In this study, we introduce a multi-modal sensing and feedback framework aimed at assisting clinicians during endovascular surgeries and catheterization procedures. This framework utilizes state-of-the-art imaging and sensing sub-systems to produce a 3D visualization of an endovascular catheter and surrounding vasculature without the need for intra-operative X-rays. Methods: The catheterization experiments within this study are conducted inside a porcine limb undergoing motions. A hybrid position-force controller of a robotically-actuated ultrasound (US) transducer for uneven porcine tissue surfaces is introduced. The tissue, vasculature, and catheter are visualized by integrated real-time US images, 3D surface imaging, and Fiber Bragg Grating (FBG) sensors. Results: During externally-induced limb motions, the vasculature and catheter can be reliably reconstructed at mean accuracies of 1.9±0.3 mm and 0.82±0.21 mm, respectively. Conclusions: The conventional use of intra-operative X-ray imaging to visualize instruments and vasculature in the human body can be reduced by employing improved diagnostic technologies that do not operate via ionizing radiation or nephrotoxic contrast agents. Significance: The presented multi-modal framework enables the radiation-free and accurate reconstruction of significant tissues and instruments involved in catheterization procedures.

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“…In the process of robot calibration, the robot's TCP poses are measured [8] and the deviations between the desired poses in the robot program and those reached in its operation are recorded. Then, complex nonlinear numerical techniques generally employ a function capable of adjusting the kinematic model and its parameters according to the measured position [9], but for this, it is necessary to use sophisticated measuring devices [10] and interrupt the machine's operation. There are also non-parametric techniques [11], using regression equations and neural networks [12] to perform this procedure.…”

Section: Introductionmentioning

confidence: 99%

Online Measuring of Robot Positions Using Inertial Measurement Units, Sensor Fusion and Artificial Intelligence

Campos

Motta

2021

IEEE Access

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An Overview of Robot Calibration

Cited by 199 publications

References 15 publications

Flange-Based Hand-Eye Calibration Using a 3D Camera With High Resolution, Accuracy, and Frame Rate

Flange-Based Hand-Eye Calibration Using a 3D Camera With High Resolution, Accuracy, and Frame Rate

Real-Time Multi-Modal Sensing and Feedback for Catheterization in Porcine Tissue

Online Measuring of Robot Positions Using Inertial Measurement Units, Sensor Fusion and Artificial Intelligence

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