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Proceedings ArticleDOI

A grasp planning for picking up an unknown object for a mobile manipulator

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TLDR
A grasp planning proposed in this paper can find a stable grasp pose from the automatically generated model which contains redundant data and the shape error of the object.
Abstract
This paper describes a grasp planning for a mobile manipulator which works in real environment. Mobile robot studies up to now that manipulate an object in real world practically use ID tag on an object or an object model which is given to the robot in advance. The authors aim to develop a mobile manipulator that can acquire an object model through video images and can manipulate the object. In this approach, the robot can manipulate an unknown object autonomously. A grasp planning proposed in this paper can find a stable grasp pose from the automatically generated model which contains redundant data and the shape error of the object. Experiments show the effectiveness of the proposed method

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Citations
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Proceedings ArticleDOI

Part-based robot grasp planning from human demonstration

TL;DR: A manipulation planning system capable of grasping objects by their parts which learns new tasks from human demonstration and the use of a topological method for shape segmentation enabling both object retrieval and part-based grasp planning according to the affordances of an object.
Proceedings ArticleDOI

Grasping of unknown objects via curvature maximization using active vision

TL;DR: A novel grasping algorithm that uses active vision as basis and the curvature information obtained from the silhouette of the object is used, which leads to a faster and still reliable grasping of the target object in 3D.
Proceedings ArticleDOI

Picking up an indicated object in a complex environment

TL;DR: An important and advantageous feature of this scheme is that the user can easily instruct the robot on how to perform the object-picking task through simple clicking operations, and the robot can execute the task without exact models of the target object and the environment being available in advance.
Book ChapterDOI

Picking up an Unknown Object through Autonomous Modeling and Grasp Planning by a Mobile Manipulator

TL;DR: A novel framework for object picking and carrying task by a mobile manipulator using the framework of a robot system, because the robot can create the 3D shape model of the object and can plan a grasp pose for the object autonomously under the condition that only the position of theobject is given to the robot.
Proceedings ArticleDOI

Fast grasping of unknown objects using force balance optimization

Qujiang Lei, +1 more
TL;DR: This paper proposes a novel grasping algorithm which does not require to build a 3D model of the object, and can quickly get the grasping position and orientation and then drive robot to the grasping point to execute grasping action.
References
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Proceedings ArticleDOI

Automatic grasp planning using shape primitives

TL;DR: This paper aims to simplify automatic grasp planning for robotic hands by modeling an object as a set of shape primitives, such as spheres, cylinders, cones and boxes, to generate aSet of grasp starting positions and pregrasp shapes that can then be tested on the object model.
Proceedings ArticleDOI

A fast and robust grasp planner for arbitrary 3D objects

TL;DR: A heuristic approach towards fast planning of precision grasps for arbitrarily shaped 3D objects is described and it is shown that only a relatively small number of grasp candidates has to be generated in order to obtain a good, although not optimal, grasp.
Proceedings ArticleDOI

Vision-based bin-picking: recognition and localization of multiple complex objects using simple visual cues

TL;DR: This work proposes an efficient solution where complex objects are sufficiently represented by simple features/cues; thus invariance to object complexity is established.
Journal ArticleDOI

Determining grasp configurations using photometric stereo and the PRISM binocular

TL;DR: A system which locates and grasps parts from a pile using photometric stereo and binocu lar stereo as vision input tools and a collision-free grasp configuration is computed and executed using the attitude and range data.
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