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Proceedings ArticleDOI

Automated monocular vision based system for picking textureless objects

TL;DR: An Autonomous Machine Vision system which grasps a textureless object from a clutter in a single plane, rearranges it for proper placement and then places it using vision using a unique vision-based pose estimation algorithm, collision free path planning and dynamic Change-Over algorithm for final placement.
Abstract: This paper proposes an Autonomous Machine Vision system which grasps a textureless object from a clutter in a single plane, rearranges it for proper placement and then places it using vision. It contributes to a unique vision-based pose estimation algorithm, collision free path planning and dynamic Change-Over algorithm for final placement.
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Journal ArticleDOI
TL;DR: A comprehensive survey of data-driven robotic visual grasping detection (DRVGD) for unknown objects is presented in this article , where object-oriented DRVGD aims for the physical information of unknown objects, such as shape, texture and rigidity, which can classify objects into conventional or challenging objects.
Abstract: This paper presents a comprehensive survey of data-driven robotic visual grasping detection (DRVGD) for unknown objects. We review both object-oriented and scene-oriented aspects, using the DRVGD for unknown objects as a guide. Object-oriented DRVGD aims for the physical information of unknown objects, such as shape, texture, and rigidity, which can classify objects into conventional or challenging objects. Scene-oriented DRVGD focuses on unstructured scenes, which are explored in two aspects based on the position relationships of object-to-object, grasping isolated or stacked objects in unstructured scenes. In addition, this paper provides a detailed review of associated grasping representations and datasets. Finally, the challenges of DRVGD and future directions are pointed out.

2 citations

Proceedings ArticleDOI
13 Jul 2016
TL;DR: This proposed system develops 3D environment model utilizing mono-vision system, which is developed through capturing multiple shots from different locations and updated continuously based on the changes in the environment and the location of the robot.
Abstract: Mobile robot system will be an important asset in our future. Mobile robot not only has to execute predefined tasks programmed with, but also it must explore the unknown environment that might be pushed to work in. In this paper we propose, implement and test a new model for mobile robot environment using mono-vision system. This proposed system develops 3D environment model utilizing mono-vision system. The model is developed through capturing multiple shots from different locations. The 3D model describes the distance and the angle of objects with respect to the robot. Finally, the mobile robot will utilize this model to navigate its environment. This is achieved through projecting the 3D model into the motion floor and identifying the obstacles surrounding the robot. Then, the robot will avoid any object in its motion line. Most importantly, the model is updated continuously based on the changes in the environment and the location of the robot.

1 citations


Cites background from "Automated monocular vision based sy..."

  • ...On the other hand, some research explored mono-vision system for mobile robots for predefined applications [14,21, 23]....

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Book ChapterDOI
01 Jan 2022
References
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01 Dec 2012
TL;DR: In this paper, a vision-based system for robotic grasping of various objects randomly organized in a bin is presented, where the objects that are about to recognize by the vision system are placed in the bin non-oriented, interlocked, jumbled and/or heavily occluding each other.
Abstract: This paper relates to a vision-based system for robotic grasping of various objects randomly organized in a bin. In other words, this work deals with the bin-picking problem where the objects that are about to recognize by the vision system are placed in a bin non-oriented, interlocked, jumbled and/or heavily occluding each other. Concerning the industrial applications, demands for a functional technology to cope with the problem are still rising. In this paper we present a short review regarding the bin-picking applications followed by selected results concerned with the system structure description, control system structure, bin-picking methodology and pilot testing projects solutions.

18 citations


"Automated monocular vision based sy..." refers background in this paper

  • ...Bin-picking invokes several problems at every stage: data acquisition, pose estimation, grasping, arrangement, collision avoidance[9] could be few a of them....

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Proceedings Article
21 May 2012
TL;DR: The basic idea is to generate several potential gripping configurations and to rate them, primarily based on the probability of collisions with any obstacles inside the sensor point cloud, and to find a suitable position for the gripper and avoid collisions with the bin or other objects.
Abstract: Bin-Picking is a complex subject. Attempts of industrial realizations are often too slow or too unreliable. Many approaches strongly focus on detecting the pose of objects inside the bin. While this is an essential part of bin-picking, industrial realizations also need to have a robust strategy to pick the detected objects out of the bin, even in complex situations. We address this issue by separating the object pose detection from the task of finding an appropriate gripping position. Our goal is to find a suitable position for the gripper and avoid collisions with the bin or other objects. This collision-free and fast gripping point determination will be presented in this paper. The basic idea is to generate several potential gripping configurations and to rate them, primarily based on the probability of collisions with any obstacles inside the sensor point cloud. The implemented approach has produced good results in experiments and industrial applications.

15 citations


"Automated monocular vision based sy..." refers background in this paper

  • ...Research on Collision free grasping[8], describes probability estimation technique for grasping an object....

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Proceedings ArticleDOI
TL;DR: The algorithm enables online pellet pose determination and pick-up using KUKA KR5 robot and a multiple-view based pose recognition system is proposed for occluded pellets.
Abstract: Pose estimation of cylindrical pellet using a single camera-in-hand configuration of a robot is discussed in this paper. Approaches to estimate pose in both isolated and an occluded environment is discussed. The pellet contour from the segmented image of the scene was compared with contours in the database to ascertain the matching orientation. For occluded pellets, a multiple-view based pose recognition system is proposed. Later, the estimated pose was communicated to the robot to enable it to pick-up the pellet. This has been experimentally implemented for cylindrical pellets and the performance is discussed. The algorithm enables online pellet pose determination and pick-up using KUKA KR5 robot.

9 citations