Grasping pose estimation for SCARA robot based on deep learning of point cloud

Wang, Zhengtuo; Xu, Yanliang; He, Quan; Fang, Zehua; Xu, Guangyue; Fu, Jianzhong

doi:10.1007/s00170-020-05257-2

Cited by 39 publications

(25 citation statements)

References 16 publications

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“…The embeddings from GPNet and SRNet are combined to refine the detected grasp. PointNetRGPE (Wang Z. et al, 2020 ) first predicts the corresponded class number from object point cloud data, which is used to fuse with point coordinates to pass into grasping pose estimation network. The network has three sub-networks based on PointNet to acquire the translation, rotation and rotation sign of grasp pose.…”

Section: End-to-end and Othersmentioning

confidence: 99%

Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning

et al. 2021

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Dexterous manipulation, especially dexterous grasping, is a primitive and crucial ability of robots that allows the implementation of performing human-like behaviors. Deploying the ability on robots enables them to assist and substitute human to accomplish more complex tasks in daily life and industrial production. A comprehensive review of the methods based on point cloud and deep learning for robotics dexterous grasping from three perspectives is given in this paper. As a new category schemes of the mainstream methods, the proposed generation-evaluation framework is the core concept of the classification. The other two classifications based on learning modes and applications are also briefly described afterwards. This review aims to afford a guideline for robotics dexterous grasping researchers and developers.

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Section: End-to-end and Othersmentioning

confidence: 99%

Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning

et al. 2021

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“…After the screw locking is completed, the end effector returns to the initial position [14,15]. At this time, the assembly line continues to move forward and the next laptop to be assembled moves to the assembly position [16,17]. e specific parameters are shown in Table 1.…”

Section: Spatial Trajectorymentioning

confidence: 99%

Machining Path Optimization of 3C Locking Robots Using Adaptive Ant Colony Optimization

Luo

Wei

et al. 2021

Mobile Information Systems

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The motion smoothness of 3C locking robot directly affects the machining performance. Improving the motion smoothness can optimize the motion trajectory and reduce the processing time. In this paper, a novel machining path optimization model including motion smoothness is built by employing the coordinate boundary of velocity and acceleration after evaluating the machining motion smoothness of the 3C locking robot. Secondly, based on the creation of the ant colony of adaptive function algorithm, the optimization model of the 3C locking robot in the situation of fixed bolt hole position and floating bolt hole position is resolved. Lastly, the proposed approach collects and analyses a huge amount of data to enable robots to make on-the-fly decisions in the middle of production, even when faced with unexpected circumstances. In the Spark distributed environment, we use the conventional K clustering technique to improve the final output utilizing clustering means. The results show that the machining path optimization of fixed hole considering the motion smoothness improves the smoothness but extends the machining path; the cooperative machining path optimization of multiregion floating bolt holes can significantly improve the motion smoothness and effectively reduce the length of the path. The research results provide theoretical support and design guidance for designers.

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“…2 (b). In those tasks, the 3D vision sensors can be utilized to capture the point cloud data (PCD) and estimate the pose of parts through the iterative closest point (ICP) algorithm [14] or using neural networks [11,13,28].…”

Section: Introductionmentioning

confidence: 99%

“…Fig. 2 (a) demonstrates some assembly tasks such as surface mount assembly [28,29] and peg-in-hole [25] tasks which only require pose compensation of 3-dimensional (3D) pose errors. The 3D pose error includes the position errors along the x-axis and y-axis and the angle error around the normal of the plane.…”

Section: Introductionmentioning

confidence: 99%

Fast 6D Object Pose Estimation of Shell Parts for Robotic Assembly

Yang

et al. 2021

Preprint

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Shell parts which have similar and close inner and outer surfaces are common in industrial manufacturing applications. In view of the 6D pose error compensation of parts in high-precision robotic assembly tasks, this work proposes a fast 6D pose estimation approach tailored for shell parts. With a binocular structured light camera, the proposed approach consists of two phases, namely initial pose estimation phase and local pose estimation phase. In the former one, an initial pose correction and translation offset methods serve to solve the local optimal estimation problem of the iterative closest point (ICP) algorithm. This problem is caused by the poorly assigned initial pose and the similar inner and outer surfaces of shell parts. In the latter one, the voxel sampling and the weighted point-to-plane ICP algorithms are applied to boost the efficiency of the pose estimation approach. With two typical shell parts, a simulation and an experiment of pose estimation are conducted to verify the effectiveness of the proposed approach. Experiment results prove that the accuracy of the pose estimation approach is 0:27mm/0:38°, and the runtime is 680ms.

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Grasping pose estimation for SCARA robot based on deep learning of point cloud

Cited by 39 publications

References 16 publications

Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning

Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning

Machining Path Optimization of 3C Locking Robots Using Adaptive Ant Colony Optimization

Fast 6D Object Pose Estimation of Shell Parts for Robotic Assembly

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