Book Chapter10.1007/978-981-10-8944-2_49
A Deep Learning-Based Robotic Grasp Detection Method
Siyuan Pi,Hong Tang,Yingying Li,Nan-feng Xiao +3 more
- 01 Jan 2019
- pp 429-436
TL;DR: This work proposes a novel method for the robotic grasp detection that gives the grasp position of a parallel-plate robotic gripper based on the deep learning model with the RGBD image of the scene.
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Abstract: Deep learning makes a great breakthrough in the field of artificial intelligence. The performance of robots on the uncertainty task can be enhanced using the deep learning. Due to the accumulative errors of the servomotors, the robot’s end-of-arm tooling (EOAT) could not grasp objects in proper position. It is worth to study robotic grasping detection with the deep learning while there has already been some successes practice in the robotics research. We propose a novel method for the robotic grasp detection that gives the grasp position of a parallel-plate robotic gripper based on the deep learning model with the RGBD image of the scene. The best model of our method archived an accuracy of 87.49% with an acceptable time speed. Our method introduces another way to solve the robotic grasping problem.
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