A 6DOF gravity compensation scheme for a phantom premium using a neural network.
Identifieur interne : 000B06 ( Ncbi/Merge ); précédent : 000B05; suivant : 000B07A 6DOF gravity compensation scheme for a phantom premium using a neural network.
Auteurs : Matthew Birtwisle [Royaume-Uni] ; Andy BulpittSource :
- Studies in health technology and informatics [ 0926-9630 ] ; 2007.
English descriptors
- KwdEn :
- MESH :
- geographic : Great Britain.
- education : General Surgery.
- Computer Simulation, Gravitation, Humans, Neural Networks (Computer), Software.
Abstract
Current uses of haptic hardware such as the Phantom Premium 6DOF for surgical simulators lack the desired interface transparency and could cause artefacts in the training regime of a student training on a simulator. This problem is addressed and two neural networks are used to find a mapping between handle coordinates and orientation and force output required to counteract gravitational forces. A close fit to the data is achieved for both networks (errors of 0.00149 and 0.0157 between training and predicted forces) and 3DOF gravity compensation is achieved. A 6DOF simulator is created but requires further work to improve it accuracy.
PubMed: 17377231
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pubmed:17377231Le document en format XML
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This problem is addressed and two neural networks are used to find a mapping between handle coordinates and orientation and force output required to counteract gravitational forces. A close fit to the data is achieved for both networks (errors of 0.00149 and 0.0157 between training and predicted forces) and 3DOF gravity compensation is achieved. 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This problem is addressed and two neural networks are used to find a mapping between handle coordinates and orientation and force output required to counteract gravitational forces. A close fit to the data is achieved for both networks (errors of 0.00149 and 0.0157 between training and predicted forces) and 3DOF gravity compensation is achieved. 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