CR-GraspNet: a robust 6-DoF grasp generation method for cleaning robot

Xinrui Wang; Lizhe Qi; Wenxuan Jiang; Yunquan Sun

doi:10.1117/12.3010635

1 December 2023 CR-GraspNet: a robust 6-DoF grasp generation method for cleaning robot

Xinrui Wang, Lizhe Qi, Wenxuan Jiang, Yunquan Sun

Proceedings Volume 12940, Third International Conference on Control and Intelligent Robotics (ICCIR 2023); 129401X (2023) https://doi.org/10.1117/12.3010635
Event: Third International Conference on Control and Intelligent Robotics (ICCIR 2023), 2023, Sipsongpanna, China

Abstract

Operational cleaning robot face numerous challenges when attempting to deftly and steadily grasp objects in cluttered scenes due to factors such as limited areas, stacked items, and restricted sensor perception. To address this issue, we propose CR-Graspnet, a six-degree-of-freedom (6-DoF) grasp generation network that utilizes point cloud contact features. This approach decouples the grasp pose in high-dimensional space by defining contact points, allowing for joint learning of contact point sampling, grasp parameter regression, and grasp quality classification. Our experimental results demonstrate the effectiveness and feasibility of this method, with a success rate of 93% achieved in single target grasping scenarios.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinrui Wang, Lizhe Qi, Wenxuan Jiang, and Yunquan Sun "CR-GraspNet: a robust 6-DoF grasp generation method for cleaning robot", Proc. SPIE 12940, Third International Conference on Control and Intelligent Robotics (ICCIR 2023), 129401X (1 December 2023); https://doi.org/10.1117/12.3010635

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