Robotic arm dexterous grasping system based on RGBD

Chunlin Ma; Zipeng Zhang; ZiWei Deng

doi:10.1117/12.2660015

3 February 2023 Robotic arm dexterous grasping system based on RGBD

Chunlin Ma, Zipeng Zhang, ZiWei Deng

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125110I (2023) https://doi.org/10.1117/12.2660015
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

Aiming at the problem that service robots have poor dexterity in grasping objects with arbitrary postures in the home environment, a dexterous grasping method adapted to objects with arbitrary postures is proposed. First, the YOLACT instance segmentation network is used to recognize and segment the target object, and the segmented target object is registered with the depth image to obtain the target point cloud. Then the target point cloud and the template in the template library are used to estimate the accurate pose of the target object by using the ICP algorithm. Finally, according to the obtained object pose, the grasping pose of the robotic arm is standardized to achieve dexterous grasping of the object. The experimental test shows that the grasping method proposed in this paper has a high total success rate of grasping objects in different postures and the grasping method is beneficial to improve the dexterity of home service robots for grasping objects, and is significant for the development of home service robots.

Citation Download Citation

Chunlin Ma, Zipeng Zhang, and ZiWei Deng "Robotic arm dexterous grasping system based on RGBD", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125110I (3 February 2023); https://doi.org/10.1117/12.2660015

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