Proceedings Article | 3 May 2011
Proc. SPIE. 8068, Bioelectronics, Biomedical, and Bioinspired Systems V; and Nanotechnology V
KEYWORDS: Sensors, Diffusion, Wavefronts, Field programmable gate arrays, Robots, Numerical analysis, Wave propagation, Differential equations, Neural networks, Neurons
Animals for surviving have developed cognitive abilities allowing them an abstract
representation of the environment. This Internal Representation (IR) could contain a huge
amount of information concerning the evolution and interactions of the elements in their
surroundings. The complexity of this information should be enough to ensure the maximum
fidelity in the representation of those aspects of the environment critical for the agent, but not so
high to prevent the management of the IR in terms of neural processes, i.e. storing, retrieving,
etc. One of the most subtle points is the inclusion of temporal information, necessary in IRs of
dynamic environments. This temporal information basically introduces the environmental
information for each moment, so the information required to generate the IR would eventually
be increased dramatically. The inclusion of this temporal information in biological neural
processes remains an open question. In this work we propose a new IR, the Compact Internal
Representation (CIR), based on the compaction of spatiotemporal information into only space,
leading to a stable structure (with no temporal dimension) suitable to be the base for complex
cognitive processes, as memory or learning. The Compact Internal Representation is especially
appropriate for be implemented in autonomous robots because it provides global strategies for
the interaction with real environments (roving robots, manipulators, etc.). This paper presents
the mathematical basis of CIR hardware implementation in the context of navigation in dynamic
environments. The aim of such implementation is the obtaining of free-collision trajectories
under the requirements of an optimal performance by means of a fast and accurate process.
