Grasping Strategies for Picking Items in an Online Shopping Warehouse

Nechyporenko, Nataliya; Morales, Antonio; Pobil, Ángel P. del

doi:10.1007/978-3-030-01370-7_60

Cited by 1 publication

(2 citation statements)

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“…Nechyporenko and co-workers conducted a study comparing the AGILE (Antipodal Grasp Identification and LEarning ) and HAF (Height Accumulated Features) methods for robot grasping [2]. This paper builds on that preliminary work; here we focus on the problem of planning the grasp and its execution, under the conditions mentioned above.…”

Section: Introductionmentioning

confidence: 99%

“…This paper proposes a new approach to this problem based on the experience gathered by our participation in two editions of the Amazon Robotics Challenge. Indeed, a preliminary version of our system [2] was used in combination with an object recognition module [23] for successful participation in the Amazon Robotics Challenge 2017, (ARC'17) [24]. As a testbed for the experiments described in this paper, we use the scenario defined by that edition of the competition, which includes realistic constraints of an online shopping warehouse.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%