Single image based camera calibration and pose estimation of the end-effector of a robot

Boby, Riby Abraham; Saha, Subir Kumar

doi:10.1109/icra.2016.7487395

Cited by 32 publications

(10 citation statements)

References 29 publications

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“…As a result, the hand-target-eye spatial attitude transformation relation is established. According to existing research [32][33][34], the camera is used to measure unidirectional pose accuracy and repeatability of industrial robots. e basic principle is to use a structured light sensor to indirectly measure the absolute posture of the robot by measuring 4 fixed reference points and to compare the real posture with the command posture to test the accuracy and repeatability of the posture.…”

Section: System Calibrationmentioning

confidence: 99%

A Point Cloud-Based Feature Recognition and Path Planning Method

Chen

Wang

Huang

2022

Shock and Vibration

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Based on the research background of in situ automatic ultrasonic phased array inspection of irregular porous castings, due to the limited in situ inspection stations, complex shape of irregular porous castings, and extreme multireflection structural features, it is necessary to identify the positioning inspection features among multiple features to be inspected and plan the optimal inspection path. This research is interested in the porous location recognition and its detection path planning of irregular porous castings. For this, a point cloud-based multifeature contour recognition and location algorithm were proposed to simultaneously extract and locate the hole feature and cylindrical feature from an irregular porous castings. Furthermore, a detection path planning method was put forward to search the shortest robot’s detection path based on the above acquired features by visual recognition and positioning technology. First, through the calibration of the industrial robot tool coordinate system and the internal and external parameters of the camera, the “EyeinHand” hand-eye conversion relationship was established. Second, the robot vision system collects the point cloud information of the area to be inspected and performs point cloud splicing, the accuracy of the original data, the removal of noise such as invalid points, outliers and internal noise points, on this basis, the boundary curve of the hole to be inspected was extracted, the cylindrical equation was fitted, its geometric center was calculated, and the central coordinates and axis direction of the contour of the hole to be inspected were obtained. Finally, all the detection paths were traversed through the multibranch tree to obtain the optimal detection path of the detection points of multiple targets. The experimental results show that the positioning accuracy of the feature of the hole to be inspected by the vision system is 0.107 mm, the aperture extraction accuracy is 0.002 mm, the cylinder fitting accuracy is 0.04 mm, and the calculation accuracy of the angle between the two axes is within 0.4. When the number of features to be inspected is different, the average moving distance can be saved by 10.7% by using the end effector after path optimization. The feasibility of in situ automatic ultrasonic phased array detection for irregular porous castings using by visual positioning is verified.

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Section: System Calibrationmentioning

confidence: 99%

A Point Cloud-Based Feature Recognition and Path Planning Method

Chen

Wang

Huang

2022

Shock and Vibration

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“…The experimental data set consists of the six slider positions and their corresponding platform pose measured using ArUco markers. 30 ArUco markers are fiducial indications accessible through the Open-source Computer Vision (OpenCV) library. These markers consist of a random black and white pattern inside a square black border, as shown in Figure 10.…”

Section: Intelligent Position Control Of the 3-ppss Parallel Manipulatormentioning

confidence: 99%

Dynamic modeling, system identification and comparative study of various control strategies for a spatial parallel manipulator

Thomas

George

Sreedharan

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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The significant challenges seen with the mathematical modeling and control of spatial parallel manipulators are its difficulty in the kinematic formulation and the inability to real-time control. The analytical approaches for the determination of the kinematic solutions are computationally expensive. This is due to the passive joints, solvability issues with non-linear equations, and inherent kinematic constraints within the manipulator architecture. Therefore, this article concentrates on an artificial neural network–based system identification approach to resolve the complexities of mathematical formulations. Moreover, the low computation time with neural networks adds up to its advantage of real-time control. Besides, this article compares the performance of a constant gain proportional–integral–derivative (PID), variable gain proportional–integral–derivative, model predictive controller, and a cascade controller with combined variable proportional–integral–derivative and model predictive controller for real-time tracking of the end-effector. The control strategies are simulated on the Simulink model of a 6-degree-of-freedom 3-PPSS (P—prismatic; S—spherical) parallel manipulator. The simulation and real-time experiments performed on the fabricated manipulator prototype indicate that the proposed cascade controller with position and velocity compensation is an appropriate method for accurate tracking along the desired path. Also, training the network using the experimentally generated data set incorporates the mechanical joint approximations and link deformities present in the fabricated model into the predicted results. In addition, this article showcases the application of Euler–Lagrangian formalism on the 3-PPSS parallel manipulator for its dynamic model incorporating the system constraints. The Lagrangian multipliers include the influence of the constraint forces acting on the manipulator platform. For completeness, the analytical model results have been verified using ADAMS for a pre-defined end-effector trajectory.

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“…Although the fiducial markers were initially developed and used in augmented reality applications [9], due to their versatility in determining their position in a non-invasive manner (by imaging them with a camera), they were quickly adopted for a different range of applications. These include kinematic calibration and visual servoing for industrial robots [10,11], robot localisation and navigation [12][13][14], SLAM (simultaneous localisation and mapping) [15][16][17] and sensor fusion [18][19][20].…”

Section: Description Of the Initial Problemmentioning

confidence: 99%

High Precision Positioning with Multi-Camera Setups: Adaptive Kalman Fusion Algorithm for Fiducial Markers

Popescu

Dumitrache

Caramihai

et al. 2020

Sensors

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The paper addresses the problem of fusing the measurements from multiple cameras in order to estimate the position of fiducial markers. The objectives are to increase the precision and to extend the working area of the system. The proposed fusion method employs an adaptive Kalman algorithm which is used for calibrating the setup of cameras as well as for estimating the pose of the marker. Special measures are taken in order to mitigate the effect of the measurement noise. The proposed method is further tested in different scenarios using a Monte Carlo simulation, whose qualitative precision results are determined and compared. The solution is designed for specific positioning and alignment tasks in physics experiments, but also, has a degree of generality that makes it suitable for a wider range of applications.

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Single image based camera calibration and pose estimation of the end-effector of a robot

Cited by 32 publications

References 29 publications

A Point Cloud-Based Feature Recognition and Path Planning Method

A Point Cloud-Based Feature Recognition and Path Planning Method

Dynamic modeling, system identification and comparative study of various control strategies for a spatial parallel manipulator

High Precision Positioning with Multi-Camera Setups: Adaptive Kalman Fusion Algorithm for Fiducial Markers

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