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- W4321063511 abstract "The problem of robotic grasping is still an unsolved problem with many approaches trying to generalize grasp predictions for unseen and dynamic environments. In this paper, we propose a complete end-to-end pipeline for the task of Deep Learning based robotic grasping on a lowcost 5-DOF arm. We explore Transfer learning approach and then train our grasping model from end-to-end. In the transfer learning approach we tried 2 base models, VGG-16 and ResNet-50. Our grasping model when ResNet-50 is used as base architecture provided better results with a testing accuracy of 83.3% while VGG-16 provided an accuracy of 78.2%. In order to test our model on a real robotic arm, we built a 5-DOF arm and added a custom parallel plate gripper. Complete ROS and Moveit support is added to our developed robotic arm. The processed RG-D image from the KinectV2 camera is given as an input to the model which predicts the 5-D grasp configuration. Required electronic system design and its PCB is built which controls the robotic arm. The predicted 5-D grasp configuration is then transformed to the object pose w.r.t the base link frame of the robot. Lastly, a ROS node is written that automates the task of picking objects lying in different positions & orientations and sends the joint angle values over pyserial communication to the microcontroller’s PCB." @default.
- W4321063511 created "2023-02-17" @default.
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- W4321063511 date "2022-11-24" @default.
- W4321063511 modified "2023-10-02" @default.
- W4321063511 title "Deep Learning based end-to-end Grasping Pipeline on a lowcost 5-DOF Robotic arm" @default.
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- W4321063511 doi "https://doi.org/10.1109/indicon56171.2022.10040180" @default.
- W4321063511 hasPublicationYear "2022" @default.
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