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• NEUROCOGNITIVE ALGORITHMS FOR MANAGING MULTI-AGENT ROBOTICS SYSTEM FOR AGRICULTURAL PURPOSES

  К.C. Bzhikhatlov, I.А. Pshenokova, А.R. Makoev

  2024-08-12

  Abstract ▼

  The main goals of the introduction of robots into agriculture are to increase efficiency and performance,
  fulfilling labor -intensive and dangerous tasks and solving the issue of lack of labor. Technological
  achievements in the field of detection and management, as well as machine learning allowed autonomous
  robots to perform more agricultural tasks. Such tasks vary at all stages of cultivation: from preparation of
  land and sowing to monitoring and harvesting. Some agricultural robots are already available, and it is
  expected that in the coming years there will be even more, since technologies for processing big data, machine vision and easy capture are becoming more accurate. Currently, the introduction of several interacting
  robots in the field is becoming increasingly relevant, since it has good prospects in reducing
  production costs and increasing operating efficiency. The purpose of this study is to develop an intellectual
  system for managing a mobile robot group based on multi -agent neurocognitive architectures. The task
  of the study is to develop neurocognitive algorithms for controlling the multi -agent robotics system of
  agricultural purposes. The work describes a multi -agent robotics complex for active plant protection
  within the framework of the Smart Field system. The concept of the management system of the group of
  mobile robots based on modeling multi -group neurocognitive architectures is presented. To ensure the
  work of the multi -agent heterogeneous group of autonomous robots, the use of a neurocognitive control
  model with the implementation of individual intellectual agents is proposed on each individual robot and
  at the bases of service or servers. At the same time, given the implementation of recursing in architecture
  itself, the task of scaling such a management system is noticeably simplified. The use of sensors and effectors
  to ensure the exchange of knowledge between robots and decision -making centers allows minimizing
  the load on the communication system and ensure a reserve of failure tolerance of the management system.
  The results obtained can be used to develop universal control systems and simplification for various
  groups of autonomous robots.