Model-Based Grasping of Unknown Objects from a Random Pile
| Autor: | François Lévesque, Philippe Cardou, SeungJae Park, Bruno Sauvet, Clément Gosselin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
TheoryofComputation_MISCELLANEOUS
0209 industrial biotechnology Control and Optimization Computer science lcsh:Mechanical engineering and machinery grasping 02 engineering and technology GeneralLiterature_MISCELLANEOUS grasp quality pile of objects object detection unknown objects 020901 industrial engineering & automation Artificial Intelligence Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Computer vision lcsh:TJ1-1570 business.industry Mechanical Engineering GRASP Object detection Robot ComputingMilieux_COMPUTERSANDSOCIETY 020201 artificial intelligence & image processing Artificial intelligence business Pile |
| Zdroj: | Robotics, Vol 8, Iss 3, p 79 (2019) Robotics; Volume 8; Issue 3; Pages: 79 |
| ISSN: | 2218-6581 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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