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Deep Recurrent Neural Networks Based Obstacle Avoidance Control for Redundant Manipulators
Obstacle avoidance is an important subject in the control of robot manipulators, but is remains challenging for robots with redundant degrees of freedom, especially when there exist complex physical constraints. In this paper, we propose a novel controller based on deep recurrent neural networks. By...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Frontiers Media S.A.
2019
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6622359/ https://www.ncbi.nlm.nih.gov/pubmed/31333442 http://dx.doi.org/10.3389/fnbot.2019.00047 |