Stiffness Estimation in Vision-Based Robotic Grasping Systems

2016 ◽  
pp. 279-288
Author(s):  
Chi-Ying Lin ◽  
Wei-Ting Hung ◽  
Ping-Jung Hsieh
Keyword(s):  
Robotic Grasping

Quality Assessment of Robotic Grasping Using Machine Regularized Leaning Algorithms

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Rami Alkhatib ◽  
Wajih Mechlawi ◽  
Rabab Kawtharani
Keyword(s):  
Quality Assessment ◽  
Robotic Grasping

Robotic grasping: from wrench space heuristics to deep learning policies

2021 ◽  
Vol 71 ◽  
pp. 102176
Author(s):  
João Pedro Carvalho de Souza ◽  
Luís F. Rocha ◽  
Paulo Moura Oliveira ◽  
A. Paulo Moreira ◽  
José Boaventura-Cunha
Keyword(s):  
Deep Learning ◽  
Robotic Grasping ◽  
Learning Policies

scholarly journals A robotic grasping approach with elliptical cone-based potential fields under disturbed scenes

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142098573
Author(s):  
Wenjie Geng ◽  
Zhiqiang Cao ◽  
Zhonghui Li ◽  
Yingying Yu ◽  
Fengshui Jing ◽  
...  
Keyword(s):  
Principal Component ◽  
Major Axis ◽  
Target Object ◽  
Potential Fields ◽  
Single Shot ◽  
Robotic Grasping ◽  
Vertical Projection ◽  
Convex Curves ◽  
Functional Representation ◽  
Continuous Potential

Vision-based grasping plays an important role in the robot providing better services. It is still challenging under disturbed scenes, where the target object cannot be directly grasped constrained by the interferences from other objects. In this article, a robotic grasping approach with firstly moving the interference objects is proposed based on elliptical cone-based potential fields. Single-shot multibox detector (SSD) is adopted to detect objects, and considering the scene complexity, Euclidean cluster is also employed to obtain the objects without being trained by SSD. And then, we acquire the vertical projection of the point cloud for each object. Considering that different objects have different shapes with respective orientation, the vertical projection is executed along its major axis acquired by the principal component analysis. On this basis, the minimum projected envelope rectangle of each object is obtained. To construct continuous potential field functions, an elliptical-based functional representation is introduced due to the better matching degree of the ellipse with the envelope rectangle among continuous closed convex curves. Guided by the design principles, including continuity, same-eccentricity equivalence, and monotonicity, the potential fields based on elliptical cone are designed. The current interference object to be grasped generates an attractive field, whereas other objects correspond to repulsive ones, and their resultant field is used to solve the best placement of the current interference object. The effectiveness of the proposed approach is verified by experiments.

Generation and Implementation of Grasping Rectangle Based on Hierarchical Shape Context and Kernel Density Estimation towards Robotic Grasping Unknown Objects

2013 ◽  
Vol 433-435 ◽  
pp. 537-544
Author(s):  
Guo Liang Kang ◽  
Shi Yin Qin
Keyword(s):  
Density Estimation ◽  
Random Forests ◽  
Kernel Density Estimation ◽  
Stereo Matching ◽  
Vision System ◽  
Kernel Density ◽  
Robotic Grasping ◽  
Shape Context ◽  
3D Space ◽  
Unknown Objects

This paper focuses on the perception step of robotic grasping unknown objects in order to get a stable grasping hypothesis. At first, hierarchical shape context feature is proposed to depict the local and global shape character of a sample point along the edges of the object. Moreover a kind of random forests classifier is adopted to recognize the grasping candidates in the image from vision system so that a 2D grasping rectangle can be generated through kernel density estimation. Finally, by means of stereo matching, the grasping rectangle can be mapped into the 3D space. Thus, the center of the grasping rectangle can be applied as the center of the gripper. The approaching vector and the grasping rectangle direction can be employed to determine the pose of the gripper. Simulated experiments showed that a reasonable and stable grasping rectangle can be generated for various unknown objects.

scholarly journals Realistic simulation of robotic grasping tasks: review and application

Procedia CIRP ◽  
2021 ◽  
Vol 104 ◽  
pp. 1704-1709
Author(s):  
Matthew Connolly ◽  
Aswin K Ramasubramanian ◽  
Matthew Kelly ◽  
Jack McEvoy ◽  
Nikolaos Papakostas
Keyword(s):  
Robotic Grasping ◽  
Realistic Simulation

scholarly journals Dealing with Ambiguity in Robotic Grasping via Multiple Predictions

2019 ◽  
pp. 38-55 ◽  
Author(s):  
Ghazal Ghazaei ◽  
Iro Laina ◽  
Christian Rupprecht ◽  
Federico Tombari ◽  
Nassir Navab ◽  
...  
Keyword(s):  
Robotic Grasping
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