Compact Internal Representation as a Protocognitive Scheme for Robots in Dynamic Environments

Abstract

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.