Abstract
In this work the center-crossing condition was integrated in artificial neural networks that incorporate synaptic delays in their connections. These synaptic delay based neural networks act as Central Pattern Generators (CPGs) for walking controllers in hexapod robotic structures. Simulated evolution is used to automatically obtain such neural controllers for walking behaviors. The optimized controllers show the time reasoning capabilities of the synaptic delay based neural networks for the temporal coordination of the hexapod joints. We compared the results against continuous time recurrent neural networks, one of the neural models most used as CPG, when proprioceptive information is used to provide fault tolerance for the required behavior.
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This work was funded by the Ministry of Economy and Competitiveness of Spain (project TIN2013-40981-R).
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Santos, J., Fernández, P. Evolved synaptic delay based neural controllers for walking patterns in hexapod robotic structures. Nat Comput 16, 201–211 (2017). https://doi.org/10.1007/s11047-016-9549-2
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DOI: https://doi.org/10.1007/s11047-016-9549-2