Method for estimating the pose of a robotic arm using a camera and calibration pattern
Industrial robots are a key component of Industry 4.0, yet accurately estimating their pose remains challenging—especially when determining the spatial relationship between the tool center point (TCP) and the working space. This study presents a hybrid pose estimation method that leverages an indust...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Ain Shams Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447925002667 |
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| Summary: | Industrial robots are a key component of Industry 4.0, yet accurately estimating their pose remains challenging—especially when determining the spatial relationship between the tool center point (TCP) and the working space. This study presents a hybrid pose estimation method that leverages an industrial camera mounted on a robotic arm's effector and a calibration pattern positioned within the working frame. To solve the resulting optimization problem, the method integrates Newton's method with a neural network (NN) pre-trained on a full camera model. Comparative experiments with state-of-the-art optimization techniques show that the proposed approach achieves superior performance in terms of both accuracy and speed. Specifically, it yields a mean position error of 0.5 mm and a mean angle error of 0.31 degrees, with a computation time of 0.14 ms. These results suggest that the method offers an efficient and accurate alternative for camera-based pose estimation in industrial settings. |
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| ISSN: | 2090-4479 |