Search

Search Results

• NATURAL LANGUAGE CONTROL OF CONSTRUCTION ROBOTIC SYSTEMS

  D.G. Makoeva , I. R. Tlupov , А. О. Shogenov

  83-93

  2025-11-10

  Abstract ▼

  The study aims to investigate the potential of natural language control systems for construction robots. It is the lack of reliable natural language processing systems that serves as a limiting factor that prevents intelligent robotics from fully realizing its potential. The work provides an overview of modern robotic construction systems that are used to facilitate and improve construction and engineering processes and tasks. What unites all these systems is the lack of natural language control. In this paper, we present principles, algorithms, and methods that allow an intelligent agent to penetrate the essence of the context of a situation unfolding in the field of construction and engineering tasks. The approach is based on a multi-agent neurocognitive architecture, which serves as a kind of tool for modeling the process of automatic interpretation of phrases taken from a limited subset of natural language. In order for an intelligent agent to correctly interpret an incoming message, it must accurately determine the conditions, actions, properties, and relationships that take place in the "intelligent agent - environment" system. Only then does the agent gain the ability to interpret the context of the current dialogue and generate statements necessary for designing cooperative behavior aimed at jointly overcoming technical obstacles. One of the most common problems requiring a solution in the rapidly developing field of robotics is the development of a dialogue control system capable of coordinating joint human-machine behavior and interpreting goals and mission conditions set out in natural language. A control system based on natural language is an integral part of an intelligent system, the foundation of which is a self-organizing multi-agent neurocognitive architecture. Its main goal is to establish seamless communication between human-machine teams so that they can jointly set, describe and successfully complete complex construction tasks. The fundamental element of the approach is multi-agency, which allows the robot's decision-making system to be flexible, adaptive and continuously expand the range of its knowledge, generating questions necessary for further work.

• MULTI-AGENT ALGORITHM FOR COLLECTING DATA FROM WEATHER STATION FOR FORECASTING PRODUCTIVITY AND CROPS CONDITION

  I.А. Pshenokova, К.C. Bzhikhatlov, А. А. Unagasov, М.А. Abazokov

  91-101

  2022-04-21

  Abstract ▼

  The weather affects the productivity and condition of crops, the requirements for the quantity
  and quality of fertilizers, as well as preventive measures to prevent diseases. Bad weather can
  affect the quality of products during transportation and storage, and hence the germination of
  seeds and planting material. Various intelligent monitoring systems are now widely used in agriculture,
  which include satellite monitoring and weather stations. In this case, the choice of a
  method for analyzing the received data and intelligent systems for their processing for predictive
  forecasting plays a fundamental role. The purpose of this study is to develop an intellectual system
  for predicting the state of the crop based on data from a weather station. A multi-agent algorithm
  for predicting the state of crops according to data from a weather station based on the selforganization
  of neurocognitive architecture was developed in this study. The description of the
  block diagram of the weather station and its sensors is given. A program algorithm has been developed
  for collecting and processing data from weather station sensors. As a result of processing,
  data on air and soil temperature, air and soil humidity, wind speed and direction, precipitation
  amount and the sum of active temperatures are sent to the intelligent decision-making system. A
  system for constructing cause-and-effect relationships is described. This system can make recommendations
  or forecasts on the condition of the crop and on the likelihood of diseases and pests in
  controlled crops.

• DEVELOPMENT OF AN INTELLIGENT ROBOTIC HARVESTING SYSTEM

  Z.V. Nagoev, О.Z. Zagazezheva, К.C. Brzhikhatlov, I.А. Mambetov

  2025-04-27

  Abstract ▼

  In the context of the need to ensure food security, the tasks of optimizing production processes in the
  agricultural sector are becoming relevant. For example, given the shortage of labor in agriculture, it is
  necessary to develop and implement robotic systems to automate the processes of plant care, harvesting
  and processing. The article presents the results of the development of an autonomous robot for picking
  apples, created on the basis of a universal anthropomorphic robot developed at the Kabardino-BalkarianScientific Center of the Russian Academy of Sciences. The robot is equipped with two multi-link manipulators
  similar to human hands, which allows it to perform complex harvesting tasks. To ensure intelligent
  control of the entire system, a multi-agent neurocognitive architecture is used, which imitates the work of
  the human brain and allows the robot to adapt to changing environmental conditions. The robot is
  equipped with a set of sensors, including video cameras, ultrasonic and infrared rangefinders, lidar and
  encoders on the manipulator drives. This allows it to accurately determine the location of apples, assess
  their ripeness and plan the trajectory of the manipulators. Particular attention is paid to the development
  of a gripper that imitates a human hand and allows you to adjust the squeezing force, which minimizes the
  risk of damage to the fruit. A multi-agent neurocognitive architecture is used to control the robot, which
  provides autonomous decision-making based on sensor data. The system is able to build a map of the area,
  determine the position of the robot and plan a route, as well as recognize apples and assess their condition.
  The article also considers the problems associated with the automation of harvesting in agriculture,
  including a lack of labor and crop losses due to improper operation of equipment. The authors emphasize
  that automation and robotization of harvesting processes have great potential, especially for crops
  that require an individual approach, such as fruits and vegetables. The presented robot demonstrates high
  efficiency in solving these problems, which is confirmed by the results of field tests. The developed system
  can be adapted to work with other crops, which makes it a universal solution for the agricultural industry