Oude Vrielink, Jeroen (2021) Learning to grasp objects in highly cluttered environments using Deep Convolutional Neural Networks. Bachelor's Thesis, Artificial Intelligence.
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Abstract
This paper evaluates the performance of the Generative Residual Convolutional Neural Network (GR-ConvNet), proposed by Kumra et al. (2020), as a grasp inference method. The performance is evaluated by testing the network’s predictive accuracy on a subset of the Cornell grasping dataset, and by conducting several rounds of the isolated, pack, and pile grasping experiments, as proposed by Kasaei et al. (2021), using a UR5e robotic arm. For this purpose, a simulation was developed in PyBullet (Coumans et al., 2020). The GR-ConvNet achieved an accuracy of 97.7% on the Cornell grasping dataset. A grasping success rate of 91.9%, 73.2%, and 55.2% was achieved, and a target success rate of 91.9%, 73.2%, and 55.2% was achieved for the isolated, pack, and pile experiments respectively.
| Item Type: | Thesis (Bachelor's Thesis) |
|---|---|
| Supervisor name: | Mohades Kasaei, S.H. |
| Degree programme: | Artificial Intelligence |
| Thesis type: | Bachelor's Thesis |
| Language: | English |
| Date Deposited: | 20 Jul 2021 13:21 |
| Last Modified: | 20 Jul 2021 13:21 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/25369 |
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