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

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 Object segmentation methods for online model acquisition to guide robotic grasping

2021 ◽  
Author(s):  
Dmitri Ignakov
Keyword(s):  
Object Segmentation ◽  
Vision System ◽  
Geometric Model ◽  
Graph Cuts ◽  
Segmentation Algorithm ◽  
Service Robots ◽  
Segmentation Method ◽  
Robotic Grasping ◽  
Model Acquisition ◽  
Segmentation Methods

A vision system is an integral component of many autonomous robots. It enables the robot to perform essential tasks such as mapping, localization, or path planning. A vision system also assists with guiding the robot's grasping and manipulation tasks. As an increased demand is placed on service robots to operate in uncontrolled environments, advanced vision systems must be created that can function effectively in visually complex and cluttered settings. This thesis presents the development of segmentation algorithms to assist in online model acquisition for guiding robotic manipulation tasks. Specifically, the focus is placed on localizing door handles to assist in robotic door opening, and on acquiring partial object models to guide robotic grasping. . First, a method for localizing a door handle of unknown geometry based on a proposed 3D segmentation method is presented. Following segmentation, localization is performed by fitting a simple box model to the segmented handle. The proposed method functions without requiring assumptions about the appearance of the handle or the door, and without a geometric model of the handle. Next, an object segmentation algorithm is developed, which combines multiple appearance (intensity and texture) and geometric (depth and curvature) cues. The algorithm is able to segment objects without utilizing any a priori appearance or geometric information in visually complex and cluttered environments. The segmentation method is based on the Conditional Random Fields (CRF) framework, and the graph cuts energy minimization technique. A simple and efficient method for initializing the proposed algorithm which overcomes graph cuts' reliance on user interaction is also developed. Finally, an improved segmentation algorithm is developed which incorporates a distance metric learning (DML) step as a means of weighing various appearance and geometric segmentation cues, allowing the method to better adapt to the available data. The improved method also models the distribution of 3D points in space as a distribution of algebraic distances from an ellipsoid fitted to the object, improving the method's ability to predict which points are likely to belong to the object or the background. Experimental validation of all methods is performed. Each method is evaluated in a realistic setting, utilizing scenarios of various complexities. Experimental results have demonstrated the effectiveness of the handle localization method, and the object segmentation methods.

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