THE DISTRIBUTED ARCHITECTURE FOR LARGE NEURAL NETWORKS (DISTAL) OF THE HUMANOID ROBOT MYON

Abstract

Humanoid robots are complex systems that require considerable processing power. This applies both for lowlevel sensorimotor loops, as well as for image processing and higher level deliberative algorithms. We present the distributed architecture DISTAL which is able to provide the processing power of large neural networks without relying on a central processor. The architecture successfully copes with runtime-metamorphoses of modular robots, such as the humanoid robot MYON, the body parts of which can be detached and reattached during runtime. We detail the implementation of DISTAL on 32-bit ARM RISC processors, describe the underlying neural byte-code (NBC) of neurons and synapses, and also depict the graphical application software BRAINDESIGNER which releases the user from program coding.