A three-fingered hand with a suction gripping system for picking various objects in cluttered narrow space

Hasegawa, Sanaé; Wada, Kazumi; Niitani, Yusuke; Okada, Kei; Inaba, Masayuki

doi:10.1109/iros.2017.8202288

Cited by 44 publications

(18 citation statements)

References 11 publications

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“…The task is moving objets from source (left bin) and destination (right bin) in Figure 11. We used the suction gripper we developed before [32], and used the centroid of point cloud extracted by the visible region segmentation for the suction point.…”

Section: B Robotic Pick-and-place Experimentsmentioning

confidence: 99%

Joint Learning of Instance and Semantic Segmentation for Robotic Pick-and-Place with Heavy Occlusions in Clutter

Wada

Okada

Inaba

2019

2019 International Conference on Robotics and Automation (ICRA)

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We present joint learning of instance and semantic segmentation for visible and occluded region masks. Sharing the feature extractor with instance occlusion segmentation, we introduce semantic occlusion segmentation into the instance segmentation model. This joint learning fuses the instanceand image-level reasoning of the mask prediction on the different segmentation tasks, which was missing in the previous work of learning instance segmentation only (instance-only).In the experiments, we evaluated the proposed joint learning comparing the instance-only learning on the test dataset. We also applied the joint learning model to 2 different types of robotic pick-and-place tasks (random and target picking) and evaluated its effectiveness to achieve real-world robotic tasks. …

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Section: B Robotic Pick-and-place Experimentsmentioning

confidence: 99%

Joint Learning of Instance and Semantic Segmentation for Robotic Pick-and-Place with Heavy Occlusions in Clutter

Wada

Okada

Inaba

2019

2019 International Conference on Robotics and Automation (ICRA)

Self Cite

View full text Add to dashboard Cite

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“…In recent years, a solid trend has emerged to apply machine and deep learning to deal with object recognition, grasp planning and other components of the pick-and-place pipeline. In some cases, convolutional networks are applied exclusively to image processing [15], whereas in other cases, learning techniques aim to obtain models that link visual perception to grasp planning. Some exhaustive techniques execute in simulation millions of grasps on artificially generated virtual objects in order to learn the relationship between shapes and successful grasps [16], or even try to connect simulated point clouds to promising suction placements on object surfaces [17,18].…”

Section: Introductionmentioning

confidence: 99%

A Practical Approach for Picking Items in an Online Shopping Warehouse

et al. 2021

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Commercially viable automated picking in unstructured environments by a robot arm remains a difficult challenge. The problem of robot grasp planning has long been around but the existing solutions tend to be limited when it comes to deploy them in open-ended realistic scenarios. Practical picking systems are called for that can handle the different properties of the objects to be manipulated, as well as the problems arising from occlusions and constrained accessibility. This paper presents a practical solution to the problem of robot picking in an online shopping warehouse by means of a novel approach that integrates a carefully selected method with a new strategy, the centroid normal approach (CNA), on a cost-effective dual-arm robotic system with two grippers specifically designed for this purpose: a two-finger gripper and a vacuum gripper. Objects identified in the scene point cloud are matched to the grasping techniques and grippers to maximize success. Extensive experimentation provides clues as to what are the reasons for success and failure. We chose as benchmark the scenario proposed by the 2017 Amazon Robotics Challenge, since it represents a realistic description of a retail shopping warehouse case; it includes many challenging constraints, such as a wide variety of different product items with a diversity of properties, which are also presented with restricted visibility and accessibility.

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“…However, in cluttered-environment applications where objects are surrounded by one another, the use of multi-fingered robotic hands is not suitable because physical contacts with the objects to be grasped may occur. In comparison, suction grippers are more suitable for grasping objects in cluttered narrow spaces [22][23][24][25][26]. However, because the suction cup is soft, suction grasping might be not stable in relatively high-speed, high-acceleration, or high-payload applications.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Multi-Function Gripper for Grasping General Objects

et al. 2019

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The development of a reliable pick-and-place system for industrial robotics is facing an urgent demand because many manual-labor works, such as piece-picking in warehouses and fulfillment centers tend toward automation. This paper presents an integrated gripper that combines a linkage-driven underactuated gripper with a suction gripping system for picking up a variety of objects in different working environments. The underactuated gripper consists of two fingers, and each finger has three degrees of freedom that are obtained by stacking one five-bar mechanism over one double parallelogram. Furthermore, each finger is actuated by two motors, both of which can be installed at the base owing to the special architecture of the proposed robotic finger. A suction cup is used to grasp objects in narrow spaces and cluttered environments. The combination of the suction and traditional linkage-driven grippers allows stable and reliable grasping under different working environments. Finally, practical experiments using a wide range of objects and under different grasping scenarios are performed to demonstrate the grasping capability of the integrated gripper.

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A three-fingered hand with a suction gripping system for picking various objects in cluttered narrow space

Cited by 44 publications

References 11 publications

Joint Learning of Instance and Semantic Segmentation for Robotic Pick-and-Place with Heavy Occlusions in Clutter

Joint Learning of Instance and Semantic Segmentation for Robotic Pick-and-Place with Heavy Occlusions in Clutter

A Practical Approach for Picking Items in an Online Shopping Warehouse

Design and Implementation of a Multi-Function Gripper for Grasping General Objects

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