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

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.