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Title: A Deep Learning-Based Autonomous Robot Manipulator for Sorting Application
bot manipulation and grasping mechanisms have received considerable attention in the recent past, leading to development of wide-range of industrial applications. This paper proposes the development of an autonomous robotic grasping system for object sorting application. RGB-D data is used by the robot for performing object detection, pose estimation, trajectory generation and object sorting tasks. The proposed approach can also handle grasping on certain objects chosen by users. Trained convolutional neural networks are used to perform object detection and determine the corresponding point cloud cluster of the object to be grasped. From the selected point cloud data, a grasp generator algorithm outputs potential grasps. A grasp filter then scores these potential grasps, and the highest-scored grasp will be chosen to execute on a real robot. A motion planner will generate collision-free trajectories to execute the chosen grasp. The experiments on AUBO robotic manipulator show the potentials of the proposed approach in the context of autonomous object sorting with robust and fast sorting performance.
Authors:
; ; ;
Award ID(s):
1919127 1846513
Publication Date:
NSF-PAR ID:
10282536
Journal Name:
2020 Fourth IEEE International Conference on Robotic Computing
Page Range or eLocation-ID:
298-305
Sponsoring Org:
National Science Foundation
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