HARDWARE-ORIENTED METHOD FOR RECONFIGURING A GROUP OF MOBILE OBJECTS

Abstract

The article describes approaches and methods for managing a group of moving objects,
characterized by the ability to autonomously make decisions about their status within the group.
Another problem of managing such a grouping is weak predictive solutions for the connectivity of
pairs of elements and their dependence on a single control center. Nanosatellites operating under
conditions of uncertainty in the internal and external environment are considered as such objects.
The goal is to ensure the coherence of the group’s apparatus through a decentralized change in
structure. It is shown that methods and algorithms for dynamic reconfiguration of a group of moving
objects predominantly use a centralized approach and a single ground control center, which is
impractical for small space exploration. A class of management methods using knowledge processing
methods and technology (artificial intelligence technology) is considered, allowing for the
identification and use of additional information about the configuration of the group. Configuration
is understood as a dual system that describes the composition and connections between neighboring elements with some quantitative assessment. The article checks the connectivity configuration
of elements to ensure continuous data transfer between a pair of arbitrary grouping
elements. The proposed reconfiguration method is hierarchical: at the upper level, reconfiguration
is based on the principles of self-organization; at the lower level, the grouping is understood as an
adaptive system that changes its state based on a trained neural network based on historical data -
time series of parameters of devices and their locations. The method is a two-level cycle of polling
each element for grouping its neighbors and drawing up a network map. This network map shows
the available connections, taking into account the current steam numbers of each device. The second
(nested) polling cycle uses control information about the future state of the device and the
connectivity of the group as a whole. Making changes to the network map instances by each device
and updating the network map instances allows, upon completion of the polling cycles, to obtain
the configuration of working devices. The results of the comparative analysis showed that management
methods based on the principles of self-organization and adaptive change in structure are
the most suitable for dynamic reconfiguration of the group. This result is possible due to the support
of forecasting steps.