6D Robotic Grasping System using Convolutional Neural Networks and Adaptive Artificial Potential Fields with Orientation Control

Viturino, Caio Cristiano Barros; Oliveira, Daniel M.; Conceição, André Gustavo Scolari; Junior, Ubiratan

doi:10.1109/lars/sbr/wre54079.2021.9605472

Cited by 5 publications

(2 citation statements)

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“…The restriction allows the planar grasp methods to represent grasps as simple oriented rectangles or keypoints in the image space, which permits directly adopting existing data-driven tools from computer vision tasks, such as object [4] or keypoint [5] detectors. However, it also neglects possible grasp poses reaching from other directions, which potentially impedes its utility in constrained environments [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Video based basketball shooting prediction and pose suggestion system

Chen¹,

Chang²,

Lin³

et al. 2023

Multimed Tools Appl

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We propose a new 6-DoF grasp pose synthesis approach from 2D/2.5D input based on keypoints. Keypointbased grasp detector from image input has demonstrated promising results in the previous study, where the additional visual information provided by color images compensates for the noisy depth perception. However, it relies heavily on accurately predicting the location of keypoints in the image space. In this paper, we devise a new grasp generation network that reduces the dependency on precise keypoint estimation. Given an RGB-D input, our network estimates both the grasp pose from keypoint detection as well as scale towards the camera. We further re-design the keypoint output space in order to mitigate the negative impact of keypoint prediction noise to Perspectiven-Point (PnP) algorithm. Experiments show that the proposed method outperforms the baseline by a large margin, validating the efficacy of our approach. Finally, despite trained on simple synthetic objects, our method demonstrate sim-to-real capacity by showing competitive results in real-world robot experiments.Code is available at: https://github.com/ivalab/KGN.

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Section: Introductionmentioning

confidence: 99%

Video based basketball shooting prediction and pose suggestion system

Chen¹,

Chang²,

Lin³

et al. 2023

Multimed Tools Appl

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“…An extended analysis of the grasping performance is given with experimental data. This work builds upon our preliminary work [32], which was only evaluated in a simulated environment [33]. According to the simulator documentation, the simulator’s collision checking system may occasionally yield wrong contact points, causing unrealistic reaction forces, vibrations, or instabilities, besides not being available for real implementations.…”

Section: Introductionmentioning

confidence: 99%

Selective 6D grasping with a collision avoidance system based on point clouds and RGB+D images

Viturino,

Conceicao

2023

Robotica

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In recent years, deep learning-based robotic grasping methods have surpassed analytical methods in grasping performance. Despite the results obtained, most of these methods use only planar grasps due to the high computational cost found in 6D grasps. However, planar grasps have spatial limitations that prevent their applicability in complex environments, such as grasping manufactured objects inside 3D printers. Furthermore, some robotic grasping techniques only generate one feasible grasp per object. However, it is necessary to obtain multiple possible grasps per object because not every grasp generated is kinematically feasible for the robot manipulator or does not collide with other close obstacles. Therefore, a new grasping pipeline is proposed to yield 6D grasps and select a specific object in the environment, preventing collisions with obstacles nearby. The grasping trials are performed in an additive manufacturing unit that has a considerable level of complexity due to the high chance of collision. The experimental results prove that it is possible to achieve a considerable success rate in grasping additive manufactured objects. The UR5 robot arm, Intel Realsense D435 camera, and Robotiq 2F-140 gripper are used to validate the proposed method in real experiments.

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