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Open AccessArticle

Model-Based Grasping of Unknown Objects from a Random Pile

Laboratoire de robotique, Département de génie mécanique, Université Laval, 1065 Avenue de la Médecine, Québec, QC G1V 0A6, Canada
Umanx, 2900, Einstein St, Quebec City, QC G1X 4B3, Canada
Cypress Robotics Corp, 65 Vicars Rd, Kamloops, BC V2C 0G3, Canada
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Robotics 2019, 8(3), 79;
Received: 8 July 2019 / Revised: 26 August 2019 / Accepted: 2 September 2019 / Published: 6 September 2019
(This article belongs to the Section Robotics & Automation)
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. View Full-Text
Keywords: grasping; grasp quality; pile of objects; object detection; unknown objects grasping; grasp quality; pile of objects; object detection; unknown objects
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MDPI and ACS Style

Sauvet, B.; Lévesque, F.; Park, S.; Cardou, P.; Gosselin, C. Model-Based Grasping of Unknown Objects from a Random Pile. Robotics 2019, 8, 79.

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    Description: Example of a robotic arm grasping unknown objects from a random pile.
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