Probabilistic approach for object bin picking approximated by cylinders

Harada, Kensuke; Nagata, Kazuyuki; Tsuji, Tokuo; Yamanobe, Natsuki; Nakamura, Akira; Kawai, Yoshihiro

doi:10.1109/icra.2013.6631103

Cited by 43 publications

(25 citation statements)

References 23 publications

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“…Typical pose estimation algorithms first compute coarse poses of objects. Several algorithms that use a set of geometric primitives such as planes, circles, and cylinders have been proposed [1], [2], [3]. Recently, pairs of oriented points used in a voting framework [4], [5], [6] have been shown to be effective for the coarse pose estimation.…”

Section: B Related Workmentioning

confidence: 99%

“…2. For example, five-finger grippers and jamming grippers [19] are all in the class of multi-finger grippers, in the sense that they grasp the target object 1 . Grippers that use suction or electromagnetic force to directly attach to an object are classified as vacuum grippers.…”

Section: A Graspable Statementioning

confidence: 99%

“…As shown in Fig. 6, the height of the target object in the depth map, h, and the depth to which the gripper advances when grasping, d, are used to define the 1 Although jamming grippers are pushed down onto an object from above, they grasp by shaping a flexible gripper to bind the object rather than by applying suction to the object. contact region W t and collision region W c for some object in the scene.…”

Section: Evaluating Graspabilitymentioning

confidence: 99%

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Fast graspability evaluation on single depth maps for bin picking with general grippers

Domae

Okuda

Taguchi

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

113

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We present a method that estimates graspability measures on a single depth map for grasping objects randomly placed in a bin. Our method represents a gripper model by using two mask images, one describing a contact region that should be filled by a target object for stable grasping, and the other describing a collision region that should not be filled by other objects to avoid collisions during grasping. The graspability measure is computed by convolving the mask images with binarized depth maps, which are thresholded differently in each region according to the minimum height of the 3D points in the region and the length of the gripper. Our method does not assume any 3-D model of objects, thus applicable to general objects. Our representation of the gripper model using the two mask images is also applicable to general grippers, such as multi-finger and vacuum grippers. We apply our method to bin picking of piled objects using a robot arm and demonstrate fast pick-and-place operations for various industrial objects. IEEE International Conference on Robotics and Automation (ICRA)This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Abstract-We present a method that estimates graspability measures on a single depth map for grasping objects randomly placed in a bin. Our method represents a gripper model by using two mask images, one describing a contact region that should be filled by a target object for stable grasping, and the other describing a collision region that should not be filled by other objects to avoid collisions during grasping. The graspability measure is computed by convolving the mask images with binarized depth maps, which are thresholded differently in each region according to the minimum height of the 3D points in the region and the length of the gripper. Our method does not assume any 3-D model of objects, thus applicable to general objects. Our representation of the gripper model using the two mask images is also applicable to general grippers, such as multi-finger and vacuum grippers. We apply our method to bin picking of piled objects using a robot arm and demonstrate fast pick-and-place operations for various industrial objects.

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Section: B Related Workmentioning

confidence: 99%

Section: A Graspable Statementioning

confidence: 99%

Section: Evaluating Graspabilitymentioning

confidence: 99%

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Fast graspability evaluation on single depth maps for bin picking with general grippers

Domae

Okuda

Taguchi

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

113

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“…Another related area to our system is bin-picking, which addresses the task of automatically picking isolating single items from a given bin [10] [11] [12] [13]. However, in the bin-picking tasks, the working space of the robot is only the given bin, which is a structured, limited and relatively easy operation area compared to our warehouse settings including a table and a shelf with 12 bins.…”

Section: Related Workmentioning

confidence: 99%

DoraPicker: An autonomous picking system for general objects

Zhang¹,

Long

Zhou³

et al. 2016

2016 IEEE International Conference on Automation Science and Engineering (CASE)

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Robots that autonomously manipulate objects within warehouses have the potential to shorten the package delivery time and improve the efficiency of the e-commerce industry. In this paper, we present a robotic system that is capable of both picking and placing general objects in warehouse scenarios. Given a target object, the robot autonomously detects it from a shelf or a table and estimates its full 6D pose. With this pose information, the robot picks the object using its gripper, and then places it into a container or at a specified location. We describe our pick-and-place system in detail while highlighting our design principles for the warehouse settings, including the perception method that leverages knowledge about its workspace, three grippers designed to handle a large variety of different objects in terms of shape, weight and material, and grasp planning in cluttered scenarios. We also present extensive experiments to evaluate the performance of our picking system and demonstrate that the robot is competent to accomplish various tasks in warehouse settings, such as picking a target item from a tight space, grasping different objects from the shelf, and performing pick-and-place tasks on the table.

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“…While there have been a number of studies on randomized bin-picking [1], [2], [7], [8], [9], [10], [11], [12], [13] and the grasp and manipulation planning of an object surrounded by many obstacles [3], [4], [5], most approaches have attempted to realize a collision-free grasping posture. If we were to apply these methods to randomized bin-picking, the planner may sometimes cannot find a feasible grasping posture, because contact between a finger and a neighboring object cannot be avoided.…”

Section: Introductionmentioning

confidence: 99%

Initial experiments on learning-based randomized bin-picking allowing finger contact with neighboring objects

Harada

Wan

Tsuji

et al. 2016

2016 IEEE International Conference on Automation Science and Engineering (CASE)

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This paper proposes a novel method for randomized bin-picking based on learning. When a two-fingered gripper tries to pick an object from the pile, a finger often contacts a neighboring object. Even if a finger contacts a neighboring object, the target object will be successfully picked depending on the configuration of neighboring objects. In our proposed method, we use the visual information on neighboring objects to train the discriminator. Corresponding to a grasping posture of an object, the discriminator predicts whether or not the pick will be successful even if a finger contacts a neighboring object. We examine two learning algorithms, the linear support vector machine (SVM) and the random forest (RF) approaches. By using both methods, we demonstrate that the picking success rate is significantly higher than with conventional methods without learning.

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Probabilistic approach for object bin picking approximated by cylinders

Cited by 43 publications

References 23 publications

Fast graspability evaluation on single depth maps for bin picking with general grippers

Fast graspability evaluation on single depth maps for bin picking with general grippers

DoraPicker: An autonomous picking system for general objects

Initial experiments on learning-based randomized bin-picking allowing finger contact with neighboring objects

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