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#5579. Development of a vision based pose estimation system for robotic machining and improving its accuracy using LSTM neural networks and sparse regression
| August 2026 | publication date |
| Proposal available till | 21-05-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Engineering |
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Abstract:
In this work, an eye to hand camera based pose estimation system is developed for robotic machining and the accuracy of the estimated pose is improved using two different approaches, namely Long Short Term Memory (LSTM) neural networks and sparse regression. To improve the accuracy obtained from the Levenberg–Marquardt (LM) based pose estimation algorithm, two distinct supervised data driven approaches are proposed which can take the dynamics into account during robotic machining through utilization of the torque information available from the sensors at each joint. The first one is a LSTM neural network and the second one is a method based on sparse regression. The proposed methods are validated by an experimental study performed using a KUKA KR240 R2900 ultra robot while machining a NAS 979 part, during which the orientation of the cutting tool was fixed, and free form milling, during which the orientation of the cutting tool continuously changed. A target object to be tracked by the camera was designed with fiducial markers to guarantee trackability with ±90°in all directions. The design of these fiducial markers guarantee the detection of at least two distinct non-parallel markers from any view, thus preventing pose estimation ambiguities. Moreover, in order to reduce the errors due to the construction of the camera target and placement of the markers on it, this work proposes a method for optimizing the positions of the corners of the fiducial markers in the object frame using a laser tracker.
Keywords:
LSTM; Machine learning; Machine vision; Pose estimation; Robotic machining; Sparse regression
Contacts :
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Abstract:
In this work, an eye to hand camera based pose estimation system is developed for robotic machining and the accuracy of the estimated pose is improved using two different approaches, namely Long Short Term Memory (LSTM) neural networks and sparse regression. To improve the accuracy obtained from the Levenberg–Marquardt (LM) based pose estimation algorithm, two distinct supervised data driven approaches are proposed which can take the dynamics into account during robotic machining through utilization of the torque information available from the sensors at each joint. The first one is a LSTM neural network and the second one is a method based on sparse regression. The proposed methods are validated by an experimental study performed using a KUKA KR240 R2900 ultra robot while machining a NAS 979 part, during which the orientation of the cutting tool was fixed, and free form milling, during which the orientation of the cutting tool continuously changed. A target object to be tracked by the camera was designed with fiducial markers to guarantee trackability with ±90°in all directions. The design of these fiducial markers guarantee the detection of at least two distinct non-parallel markers from any view, thus preventing pose estimation ambiguities. Moreover, in order to reduce the errors due to the construction of the camera target and placement of the markers on it, this work proposes a method for optimizing the positions of the corners of the fiducial markers in the object frame using a laser tracker.
Keywords:
LSTM; Machine learning; Machine vision; Pose estimation; Robotic machining; Sparse regression
Contacts :
