scholarly journals Model-Based Grasping of Unknown Objects from a Random Pile

Robotics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 79 ◽  
Author(s):  
Bruno Sauvet ◽  
François Lévesque ◽  
SeungJae Park ◽  
Philippe Cardou ◽  
Clément Gosselin
Keyword(s):  
Simple Approach ◽  
Decision Algorithm ◽  
Easy Task ◽  
Model Based ◽  
Robustness Index ◽  
Unknown Objects ◽  
Unknown Object

Grasping an unknown object in a pile is no easy task for a robot—it is often difficult to distinguish different objects; objects occlude one another; object proximity limits the number of feasible grasps available; and so forth. In this paper, we propose a simple approach to grasping unknown objects one by one from a random pile. The proposed method is divided into three main actions—over-segmentation of the images, a decision algorithm and ranking according to a grasp robustness index. Thus, the robot is able to distinguish the objects from the pile, choose the best candidate for grasping among these objects, and pick the most robust grasp for this candidate. With this approach, we can clear out a random pile of unknown objects, as shown in the experiments reported herein.

scholarly journals Pose Estimation of Uncooperative Unknown Space Objects from a Single Image

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaoyuan Ren ◽  
Libing Jiang ◽  
Zhuang Wang
Keyword(s):  
Pose Estimation ◽  
Experimental Results ◽  
Single Image ◽  
Space Object ◽  
Shape Model ◽  
Structure Information ◽  
Space Objects ◽  
Specific Object ◽  
Unknown Objects ◽  
Unknown Object

Estimating the 3D pose of the space object from a single image is an important but challenging work. Most of the existing methods estimate the 3D pose of known space objects and assume that the detailed geometry of a specific object is known. These methods are not available for unknown objects without the known geometry of the object. In contrast to previous works, this paper devotes to estimate the 3D pose of the unknown space object from a single image. Our method estimates not only the pose but also the shape of the unknown object from a single image. In this paper, a hierarchical shape model is proposed to represent the prior structure information of typical space objects. On this basis, the parameters of the pose and shape are estimated simultaneously for unknown space objects. Experimental results demonstrate the effectiveness of our method to estimate the 3D pose and infer the geometry of unknown typical space objects from a single image. Moreover, experimental results show the advantage of our approach over the methods relying on the known geometry of the object.

scholarly journals Estimating Weight of Unknown Objects Using Active Thermography

Robotics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 92 ◽  
Author(s):  
Tamas Aujeszky ◽  
Georgios Korres ◽  
Mohamad Eid ◽  
Farshad Khorrami
Keyword(s):  
Neural Networks ◽  
Physical Properties ◽  
Infrared Thermography ◽  
Material Characterization ◽  
Autonomous Robots ◽  
Density Property ◽  
Active Thermography ◽  
Density Values ◽  
Unknown Objects ◽  
Unknown Object

Successful manipulation of unknown objects requires an understanding of their physical properties. Infrared thermography has the potential to provide real-time, contactless material characterization for unknown objects. In this paper, we propose an approach that utilizes active thermography and custom multi-channel neural networks to perform classification between samples and regression towards the density property. With the help of an off-the-shelf technology to estimate the volume of the object, the proposed approach is capable of estimating the weight of the unknown object. We show the efficacy of the infrared thermography approach to a set of ten commonly used materials to achieve a 99.1% R 2 -fit for predicted versus actual density values. The system can be used with tele-operated or autonomous robots to optimize grasping techniques for unknown objects without touching them.

scholarly journals Real-time grasping strategies using event camera

2022 ◽  
Author(s):  
Xiaoqian Huang ◽  
Mohamad Halwani ◽  
Rajkumar Muthusamy ◽  
Abdulla Ayyad ◽  
Dewald Swart ◽  
...  
Keyword(s):  
Visual Servoing ◽  
Sampling Rate ◽  
Motion Blur ◽  
Grasp Planning ◽  
Robotic Vision ◽  
Model Based ◽  
Model Free ◽  
Event Camera ◽  
Unknown Objects ◽  
Event Based

AbstractRobotic vision plays a key role for perceiving the environment in grasping applications. However, the conventional framed-based robotic vision, suffering from motion blur and low sampling rate, may not meet the automation needs of evolving industrial requirements. This paper, for the first time, proposes an event-based robotic grasping framework for multiple known and unknown objects in a cluttered scene. With advantages of microsecond-level sampling rate and no motion blur of event camera, the model-based and model-free approaches are developed for known and unknown objects’ grasping respectively. The event-based multi-view approach is used to localize the objects in the scene in the model-based approach, and then point cloud processing is utilized to cluster and register the objects. The proposed model-free approach, on the other hand, utilizes the developed event-based object segmentation, visual servoing and grasp planning to localize, align to, and grasp the targeting object. Using a UR10 robot with an eye-in-hand neuromorphic camera and a Barrett hand gripper, the proposed approaches are experimentally validated with objects of different sizes. Furthermore, it demonstrates robustness and a significant advantage over grasping with a traditional frame-based camera in low-light conditions.

scholarly journals An improved consumer goods market model-based vertical handoff decision algorithm

2012 ◽  
Vol 9 (4) ◽  
pp. 234-239 ◽  
Author(s):  
Xingwei Liu ◽  
Tao Yang ◽  
Li Wang ◽  
Xu Chen ◽  
Hui Bo ◽  
...  
Keyword(s):  
Consumer Goods ◽  
Market Model ◽  
Vertical Handoff ◽  
Decision Algorithm ◽  
Model Based ◽  
Goods Market

A model-based scooping grasp for the autonomous picking of unknown objects with a two-fingered gripper

2018 ◽  
Vol 106 ◽  
pp. 14-25 ◽  
Author(s):  
François Lévesque ◽  
Bruno Sauvet ◽  
Philippe Cardou ◽  
Clément Gosselin
Keyword(s):  
Model Based ◽  
Unknown Objects

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Influence of X-Ray Induced Fluorescence on Energy Dispersive X-Ray Analysis of Thin Foils

1977 ◽  
Vol 35 ◽  
pp. 328-329
Author(s):  
E. A. Kenik ◽  
J. Bentley
Keyword(s):  
Thin Foil ◽  
Electron Excitation ◽  
Simple Approach ◽  
Foil Thickness ◽  
X Rays ◽  
X Ray ◽  
Hole Spectrum ◽  
Illumination System ◽  
Thin Foils ◽  
Intensity Ratios

Cliff and Lorimer (1) have proposed a simple approach to thin foil x-ray analy sis based on the ratio of x-ray peak intensities. However, there are several experimental pitfalls which must be recognized in obtaining the desired x-ray intensities. Undesirable x-ray induced fluorescence of the specimen can result from various mechanisms and leads to x-ray intensities not characteristic of electron excitation and further results in incorrect intensity ratios.In measuring the x-ray intensity ratio for NiAl as a function of foil thickness, Zaluzec and Fraser (2) found the ratio was not constant for thicknesses where absorption could be neglected. They demonstrated that this effect originated from x-ray induced fluorescence by blocking the beam with lead foil. The primary x-rays arise in the illumination system and result in varying intensity ratios and a finite x-ray spectrum even when the specimen is not intercepting the electron beam, an ‘in-hole’ spectrum. We have developed a second technique for detecting x-ray induced fluorescence based on the magnitude of the ‘in-hole’ spectrum with different filament emission currents and condenser apertures.

Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

2001 ◽  
Vol 7 (S2) ◽  
pp. 578-579
Author(s):  
David W. Knowles ◽  
Sophie A. Lelièvre ◽  
Carlos Ortiz de Solόrzano ◽  
Stephen J. Lockett ◽  
Mina J. Bissell ◽  
...  
Keyword(s):  
Image Analysis ◽  
Mammary Epithelial Cells ◽  
Nuclear Organization ◽  
Critical Role ◽  
Quantitative Model ◽  
Mammary Epithelial ◽  
Cell Phenotype ◽  
Proliferating Cells ◽  
Mitotic Apparatus ◽  
Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification
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