Toward Grasping Against the Environment: Locking Polygonal Objects Against a Wall Using Two-Finger Robot Hands

Bunis, Hallel A.; Rimon, Elon

doi:10.1109/lra.2018.2882865

Cited by 3 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to realize the object grasping operation, it is necessary to establish a mathematical model of the robot grasping, abstract the actual physical problems, use the specific mathematical parameters to represent the grasping target information of the robot, and then determine the GD network structure and the composition of the loss function [9]. When building an end-to-end grasp detection network, the forward prediction operation inputs the RGB image of multiple objects in the scene, and then directly outputs the grasp parameters of the object, while the grasp depth of the object is obtained from the depth map of the object; Take the target grasping pose obtained by the system for coordinate transformation, and then control the gripper at the end of the robotic arm to complete the grasping operation [10][11].…”

Section: Multi-target Object Grasping and Recognition Algorithm Based...mentioning

confidence: 99%

Object Recognition and Grasping Detection Method of Construction Machinery Robot Relying on Deep Learning

2021

KME

View full text Add to dashboard Cite

At present, robots are widely used in the field of machinery manufacturing, which improves the production speed on the basis of ensuring the safety of workers. As a result, the traditional mode of enterprises based on manual operation has begun to be transformed into industrial robots as the main body. Construction machinery robots need to capture information about the surrounding environment in production operations. Traditional object recognition methods cannot adapt to complex working environments. Therefore, how to effectively identify target objects and successfully grasp objects has become a challenge for robots. In addition, the grasping detection(GD) method required by the robot to complete the task also relies on the known information of the target object and cannot effectively deal with the complex and changeable unknown environment. To this end, this paper designs a robot GD system based on deep learning, constructs a GD model and system framework through a convolutional neural network(CNN), and introduces a multi-target object grasping recognition algorithm to improve the grasping accuracy of the robot. The simulation experiment of the GD system proves that the accuracy rate of the system successfully grasping objects is over 95%.

show abstract

Section: Multi-target Object Grasping and Recognition Algorithm Based...mentioning

confidence: 99%

Object Recognition and Grasping Detection Method of Construction Machinery Robot Relying on Deep Learning

2021

KME

View full text Add to dashboard Cite

show abstract

Contact Space Computation of Two-Finger Gravity Based Caging Grasps Security Measure

Shirizly

Rimon

Wan

2021

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

Selection of Secure Gravity-Based Caging Grasps of Planar Objects: Robustness and Experimental Validation

Shirizly,

Rimon

2024

IEEE Trans. Robot.

View full text Add to dashboard Cite

Toward Grasping Against the Environment: Locking Polygonal Objects Against a Wall Using Two-Finger Robot Hands

Cited by 3 publications

References 11 publications

Object Recognition and Grasping Detection Method of Construction Machinery Robot Relying on Deep Learning

Object Recognition and Grasping Detection Method of Construction Machinery Robot Relying on Deep Learning

Contact Space Computation of Two-Finger Gravity Based Caging Grasps Security Measure

Selection of Secure Gravity-Based Caging Grasps of Planar Objects: Robustness and Experimental Validation

Contact Info

Product

Resources

About