On the improvement of calibration accuracy of parallel robots – modeling and optimization

Bentaleb, Toufik; Iqbal, Jamshed

doi:10.15632/jtam-pl/115863

Cited by 12 publications

(7 citation statements)

References 21 publications

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“…Reaching the highest levels of repeatability and precision for pose determination in robotic manipulators in automated manufacturing is currently a practical problem [1]. Even slight deviations from nominal robot geometry can produce substantial errors at the end effector, which can be greater than 0.5 inches at the end effector of a 6 ft robot arm.…”

Section: Introductionmentioning

confidence: 99%

“…In [28], they propose a new calibration method for a 5-Degrees-Of-Freedom (DOF) hybrid robot, concentrating particularly on addressing the contradiction between measurement efficiency and calibration accuracy, and real-time compensation with high precision . The approach involves two successive steps: (1) an error-prediction model based on a back-propagation neural network (BPNN) combined with the Denavit-Hartenberg (D-H) method established by the pose error decomposition strategy; and (2) an embedded joint error compensator based on a BPNN designed to achieve real-time compensation with high precision. In [29], they describe the development of a calibration procedure for a 5-DOF serial robot using a laser tracker.…”

Section: Introductionmentioning

confidence: 99%

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Pose Determination System for a Serial Robot Manipulator Based on Artificial Neural Networks

Rodriguez-Miranda

Yanez-Mendiola²,

Calzada-Ledesma

et al. 2023

Machines

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Achieving the highest levels of repeatability and precision, especially in robot manipulators applied in automation manufacturing, is a practical pose-recognition problem in robotics. Deviations from nominal robot geometry could produce substantial errors at the end effector, which can be more than 0.5 inches for a 6 ft robot arm. In this research, a pose-recognition system is developed for estimating the position of each robot joint and end-effector pose using image processing. To generate the joint angle, the system is developed via the modeling of a pose obtained by combining a convolutional neural network (CNN) and a multi-layer perceptron network (MLP). The CNN categorizes the input image generated by a remote monocular camera and generates a classification probability vector. The MLP generates a multiple linear regression model based on the probability vector generated by a CNN and describes the values of each joint angle. The proposed model is compared with the P-n-Perspective problem-solving method, which is based on marker tracking using ArUco markers and the encoder values. The system was verified using a robot manipulator with four degrees of freedom. Additionally, the proposed method exhibits superior performance in terms of joint-by-joint error, with an absolute error that is three units less than that of the computer vision method. Furthermore, when evaluating the end-effector pose, the proposed method showed a lower average standard deviation of 9mm compared with the computer vision method, which had a standard deviation of 13 mm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pose Determination System for a Serial Robot Manipulator Based on Artificial Neural Networks

Rodriguez-Miranda

Yanez-Mendiola²,

Calzada-Ledesma

et al. 2023

Machines

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“…Traditional control strategies are hard to implement on parallel robots, due to: the robot’s multiple solutions to the direct kinematic problem and modeling errors in the Jacobian as a result of inaccurate geometric calibration. Research on parallel robots has focused on mitigating the effects of these drawbacks on their precision and ease of control [ 5 , 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Velocity-Based Control in a Sensor Space for Parallel Manipulators

Loredo

Maya

González

et al. 2022

Sensors

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It is a challenging task to track objects moving along an unknown trajectory. Conventional model-based controllers require detailed knowledge of a robot’s kinematics and the target’s trajectory. Tracking precision heavily relies on kinematics to infer the trajectory. Control implementation in parallel robots is especially difficult due to their complex kinematics. Vision-based controllers are robust to uncertainties of a robot’s kinematic model since they can correct end-point trajectories as error estimates become available. Robustness is guaranteed by taking the vision sensor’s model into account when designing the control law. All camera space manipulation (CSM) models in the literature are position-based, where the mapping between the end effector position in the Cartesian space and sensor space is established. Such models are not appropriate for tracking moving targets because the relationship between the target and the end effector is a fixed point. The present work builds upon the literature by presenting a novel CSM velocity-based control that establishes a relationship between a movable trajectory and the end effector position. Its efficacy is shown on a Delta-type parallel robot. Three types of experiments were performed: (a) static tracking (average error of 1.09 mm); (b) constant speed linear trajectory tracking—speeds of 7, 9.5, and 12 cm/s—(tracking errors of 8.89, 11.76, and 18.65 mm, respectively); (c) freehand trajectory tracking (max tracking errors of 11.79 mm during motion and max static positioning errors of 1.44 mm once the object stopped). The resulting control cycle time was 48 ms. The results obtained show a reduction in the tracking errors for this robot with respect to previously published control strategies.

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“…For a robot to obtain an accurate estimate of the 3D position and orientation of a part relative to its own base within the work volume, it is necessary to know the relative position and orientation between the hand and the robot base, between the camera and the hand, and between the object and the camera. These three tasks require the calibration of robot [ 10 , 11 ], camera [ 12 , 13 ], and robot hand-to-camera (hand-eye) [ 14 , 15 ] to obtain the necessary accuracy. Robot calibration is needed because, even though robots have very good repeatability, they are poor when it comes to absolute accuracy, due to inherent differences between the ideal and actual kinematic parameters.…”

Section: Introductionmentioning

confidence: 99%

Accuracy evaluation of hand-eye calibration techniques for vision-guided robots

Enebuse

Ibrahim²,

Foo³

et al. 2022

PLoS ONE

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Hand-eye calibration is an important step in controlling a vision-guided robot in applications like part assembly, bin picking and inspection operations etc. Many methods for estimating hand-eye transformations have been proposed in literature with varying degrees of complexity and accuracy. However, the success of a vision-guided application is highly impacted by the accuracy the hand-eye calibration of the vision system with the robot. The level of this accuracy depends on several factors such as rotation and translation noise, rotation and translation motion range that must be considered during calibration. Previous studies and benchmarking of the proposed algorithms have largely been focused on the combined effect of rotation and translation noise. This study provides insight on the impact of rotation and translation noise acting in isolation on the hand-eye calibration accuracy. This deviates from the most common method of assessing hand-eye calibration accuracy based on pose noise (combined rotation and translation noise). We also evaluated the impact of the robot motion range used during the hand-eye calibration operation which is rarely considered. We provide quantitative evaluation of our study using six commonly used algorithms from an implementation perspective. We comparatively analyse the performance of these algorithms through simulation case studies and experimental validation using the Universal Robot’s UR5e physical robots. Our results show that these different algorithms perform differently when the noise conditions vary rather than following a general trend. For example, the simultaneous methods are more resistant to rotation noise, whereas the separate methods are better at dealing with translation noise. Additionally, while increasing the robot rotation motion span during calibration enhances the accuracy of the separate methods, it has a negative effect on the simultaneous methods. Conversely, increasing the translation motion range improves the accuracy of simultaneous methods but degrades the accuracy of the separate methods. These findings suggest that those conditions should be considered when benchmarking algorithms or performing a calibration process for enhanced accuracy.

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On the improvement of calibration accuracy of parallel robots – modeling and optimization

Cited by 12 publications

References 21 publications

Pose Determination System for a Serial Robot Manipulator Based on Artificial Neural Networks

Pose Determination System for a Serial Robot Manipulator Based on Artificial Neural Networks

A Novel Velocity-Based Control in a Sensor Space for Parallel Manipulators

Accuracy evaluation of hand-eye calibration techniques for vision-guided robots

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