Application and use of quality metrics for the prediction of grasp success and evaluation of artificial hands
Artificial manipulation has been one of the main areas of interest in robotics for decades. Finding a proper grasp to seize objects and design robotic hands capable of such grasps are two of the main problems in such field. This thesis analyze the use of quality metrics to evaluate grasp hypothesis....
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| Formato: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | España |
| Recursos: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/663158 |
| Acesso em linha: | http://hdl.handle.net/10803/663158 http://dx.doi.org/10.6035/14101.2018.176497 |
| Access Level: | acceso abierto |
| Palavra-chave: | Grasping Quality Metrics Grasp Prediction Artificial hands Grasp Evaluation Machine Learning Enginyeria, indústria i construcció 68 |
| Resumo: | Artificial manipulation has been one of the main areas of interest in robotics for decades. Finding a proper grasp to seize objects and design robotic hands capable of such grasps are two of the main problems in such field. This thesis analyze the use of quality metrics to evaluate grasp hypothesis. Different classification algorithms are used with such metrics to predict the success of grasp hypothesis. These algorithms are applied to evaluate artifical hands and their performance to grasp objects. |
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