Name
Abstract | ||
|---|---|---|
A new-generation general-purpose robot gripper system which applies an artificial neural network to guide a three-finger gripper has been designed. The simulation of the core part of the whole system, i.e. optimally placing three fingers for a stable grasp using the Hopfield net, has been conducted. The results obtained show that this scheme behaves in a promising fashion. The actual computation time is usually within several seconds if implemented in an analog neural net, making the real application attractive |
Year | DOI | Venue |
|---|---|---|
1990 | 10.1109/IJCNN.1990.137866 | IJCNN |
Keywords | Field | DocType |
computerised control,neural nets,position control,robots,hopfield net,analog neural net,artificial neural network,robot grippers,simulation,stable grasp,three-finger gripper,neural network,neural net | GRASP,Computer science,Artificial intelligence,Artificial neural network,Robot,Grippers,Hopfield network,Machine learning,Computation | Conference |
Citations | PageRank | References |
4 | 0.74 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xu, G. | 1 | 4 | 0.74 |
| Scherrer, H. | 2 | 4 | 0.74 |
| Schweitzer, G. | 3 | 4 | 0.74 |