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Abstract
  • The work presented in this paper deals with the development of a methodology
    for resource-efficient behaviour synthesis on autonomous systems. In this context, a definition
    of a maximal problem with respect to the resources of a given system is introduced. It
    is elucidated by means of an exemplary implementation of the solution to such a problem
    using the mini-robot Khepera as the experimental platform. The described task consists of
    exploring an unknown and dynamically changing environment, collecting and transporting
    objects, which are associated with light-sources, and navigating to a home-base. The critical
    point is represented by the accumulated positioning errors in odometrical path-integration
    due to slippage. Therefore, adaptive sensor calibration using a specific variant of Kohonen’s
    algorithm is applied in two cases to extract symbolic, e.g. geometric, information from the
    sub-symbolic sensor data, which is used to enhance position control by landmark mapping
    and orientation. In order to successfully handle the arising complex interactions, a heterogeneous
    control-architecture based on a parallel implementation of basic behaviours coupled
    by a rule-based central unit is proposed.
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