Abstract
This article describes an FPGA (Field Programmable Gate Array) based hardware implementation of a genetic controller to be applied for the evolution of an Artificial Neural Network (ANN) [3] for collision-free navigation task of mobile robots. The adaptive nature of ANN enables it to train itself while the robot interacts with the environment. In addition to online training, the genetic evolution in neuron bits will be examined in an experiment to understand the interaction between evolution and lifetime adaptation of the ANN. The concept of chromosome for navigation task, design techniques of various blocks inside the GA controller will be elaborately described here.
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Hannan Bin Azhar, M.A., Dimond, K.R. (2003). Hardware Implementation of a Genetic Controller and Effects of Training on Evolution. In: Tyrrell, A.M., Haddow, P.C., Torresen, J. (eds) Evolvable Systems: From Biology to Hardware. ICES 2003. Lecture Notes in Computer Science, vol 2606. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36553-2_31
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DOI: https://doi.org/10.1007/3-540-36553-2_31
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