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Effect area diagram of estimated feature point for the dynamic workpiece using FPFH.

Effect area diagram of estimated feature point for the dynamic workpiece using FPFH.

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Figure 1. Experiment device setup of stereo vision scanning for dynamic...
Figure 2. Calibration model of a camera in the dynamic workpiece...
Figure 3. Schematic diagram of two cameras' stereo calibration for...
Figure 5. Effect area diagram of estimated feature point for the...
+11 Figure 6. Construction of the target object gripping pose.
Dynamic Workpiece Modeling with Robotic Pick-Place Based on Stereo Vision Scanning Using Fast Point-Feature Histogram Algorithm
Article
Full-text available
  • Dec 2021
In the era of rapid development in industry, an automatic production line is the fundamental and crucial mission for robotic pick-place. However, most production works for picking and placing workpieces are still manual operations in the stamping industry. Therefore, an intelligent system that is fully automatic with robotic pick-place instead of h...
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Context 1
... the weight, w ki , indicates the distance between point p and the neighbor point p ki . Figure 5 shows the effect area of the FPFH algorithm. In the figure, the query point p only linked with its direct neighbors p k (inside the red-dashed circle). ...

Citations

... Calibration for high-precision robotic machining was achieved by selecting rotation parameters that have a significant impact on solution accuracy. Do et al. addressed the recognition of six degrees of freedom for workpieces using an RGB-D camera to automate robot picking and placing for production works [22]. For accurate grasping, robot error compensation using a checkerboard was introduced. ...
Hand-Eye Calibration via Linear and Nonlinear Regressions
Article
Full-text available
  • May 2023
For a robot to pick up an object viewed by a camera, the object’s position in the image coordinate system must be converted to the robot coordinate system. Recently, a neural network-based method was proposed to achieve this task. This methodology can accurately convert the object’s position despite errors and disturbances that arise in a real-world environment, such as the deflection of a robot arm triggered by changes in the robot’s posture. However, this method has some drawbacks, such as the need for significant effort in model selection, hyperparameter tuning, and lack of stability and interpretability in the learning results. To address these issues, a method involving linear and nonlinear regressions is proposed. First, linear regression is employed to convert the object’s position from the image coordinate system to the robot base coordinate system. Next, B-splines-based nonlinear regression is applied to address the errors and disturbances that occur in a real-world environment. Since this approach is more stable and has better calibration performance with interpretability as opposed to the recent method, it is more practical. In the experiment, calibration results were incorporated into a robot, and its performance was evaluated quantitatively. The proposed method achieved a mean position error of 0.5 mm, while the neural network-based method achieved an error of 1.1 mm.
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