Planning and Control Method Based on Fuzzy Logic for Intelligent Machine

Abstract

Intelligent Machine (IM) control is based on a complex solution of three tasks: perception of data from sensors, planning actions and decisions making (DM) in accordance with the actual plan and process’s history. The solution to this problem is the main challenge of the IM. Spatio-temporal datasets from a lot of heterogeneous sensors, non-deterministic, dynamic, and partially observable characteristics of environment impose additional restrictions on the DM technology. Fuzzy logic systems (FLS) are not used in such conditions due to the large dimension of the problem. The two-stage Computing with Words (CWW) approach overcomes the dimensionality problem by using abstraction engine at the first stage, which maps the meaning of data from a lot of sensors to the meaning of a few words. In this article the CWW technology is extended by a Short-Term Memory (STM) and Long-Term Memory (LTM) models. The STM stores a time sequence of data in the form of an ordered sequence of events. The LTM stores an action plan in the form an ordered sequence of plan stages. Both are formalized in the form of a flat vector field each element of which is represented on word fuzzy characteristics. The STM model is supported by the footprint blur algorithm, and LTM model is supported by dynamic planning algorithm. An example of the use of STM and LTM data in the FLS when decisions making by IM, is given.