Abstract
In scenarios such as automated sorting in logistics warehouses, automated assembly on industrial production lines, and transportation of rescue tasks and items, it is necessary to quickly grasp and place objects. Although existing methods can achieve simple object grabbing and placement, when selecting specific objects for grabbing, the grabbing strategy is usually based on the priority of the target object recognition matching score, and the grabbing strategy is relatively single. This paper introduces a robot grabbing system based on the optimization of structured light 3D imaging machine vision guidance strategy. First, the pose of all grabbing objects in the scene is estimated, and the optimal grabbing sequence is calculated, so as to achieve rapid classified grabbing and placement of sorted objects. First, the objects in the scene are scanned by a structured light camera, and the captured objects are modeled. Secondly, the optimized point-to-point feature matching algorithm is used to estimate the pose of scene objects and obtain item pose information. Finally, the robot plans the optimal order of grasping objects based on the optimized Monte Carlo tree search algorithm. This grabbing strategy can consider the distance of the grabbing path and the relevant weights of the matching score before conducting the operation.
The paper is supported by the Tertiary Education Scientific research project of Guangzhou Municipal Education Bureau under grant No. 202235165.
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Lin, J. et al. (2023). A Grasping System with Structured Light 3D Machine Vision Guided Strategy Optimization. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14118. Springer, Cham. https://doi.org/10.1007/978-3-031-40286-9_31
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