A Benchmarking Platform for Learning-Based Grasp Synthesis Methodologies

Vuuren, Jacques Janse van; Tang, Liqiong; Al‐Bahadly, Ibrahim; Arif, Khalid Mahmood

doi:10.1007/s10846-021-01410-5

Cited by 3 publications

(1 citation statement)

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“…The reason is that each experiment differs from the other by using different types and numbers of objects, physical hardware, robot arms, grippers, and cameras. Therefore, different authors recently published benchmark procedures for analyzing the grasping performance in distinct scenarios [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Selective 6D grasping with a collision avoidance system based on point clouds and RGB+D images

Viturino,

Conceicao

2023

Robotica

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In recent years, deep learning-based robotic grasping methods have surpassed analytical methods in grasping performance. Despite the results obtained, most of these methods use only planar grasps due to the high computational cost found in 6D grasps. However, planar grasps have spatial limitations that prevent their applicability in complex environments, such as grasping manufactured objects inside 3D printers. Furthermore, some robotic grasping techniques only generate one feasible grasp per object. However, it is necessary to obtain multiple possible grasps per object because not every grasp generated is kinematically feasible for the robot manipulator or does not collide with other close obstacles. Therefore, a new grasping pipeline is proposed to yield 6D grasps and select a specific object in the environment, preventing collisions with obstacles nearby. The grasping trials are performed in an additive manufacturing unit that has a considerable level of complexity due to the high chance of collision. The experimental results prove that it is possible to achieve a considerable success rate in grasping additive manufactured objects. The UR5 robot arm, Intel Realsense D435 camera, and Robotiq 2F-140 gripper are used to validate the proposed method in real experiments.

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Section: Introductionmentioning

confidence: 99%