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• 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.

• MULTI-AGENT ALGORITHM FOR AUTOMATIC DETECTION AND TRACKING OF NON-DETERMINISTIC OBJECTS

  V.A. Tupikov, V.A. Pavlova, V.A. Bondarenko, A.I. Lizin, D.K. Eltsova, M.V. Sozinova

  2020-07-10

  Abstract ▼

  In order to develop a robust algorithm for the automatic detection and tracking of non-deterministic objects for embedded computing systems, in this work, a study and analysis in the field of state-of-the-art general-purpose automatic tracking algorithms is performed. The most successful of those algorithms suitable for long-term stable automatic tracking of objects (without a priori knowledge of the type of object being tracked) have already gone beyond solving exclu-sively tracking problems, and include a synergistic combination of several heterogeneous tracking algorithms, as well as at least one automatic detection and / or classification algorithm. Thus, the authors of the article conclude that the most stable modern automatic tracking algorithms are a multi-agent system that makes a decision about the current position, size and other parameters of the tracked object image based on intelligent voting of system’s submodules that independently monitor the object and form its model. Individual models of each of the submodules are updated based on the results of a collective decision. The authors of the study identified the most effective of the applied basic algorithms suitable for use in embedded computing systems of robotic systems, and developed a new multi-agent algorithm for the automatic detection and tracking of non-deterministic objects. The presented multi-agent algorithm includes a submodule for extracting and matching key points in images, a clustering and filtering submodule for key points using the DBSCAN algorithm, a tracking submodule based on the optical flow calculation algorithm, and a key point classification submodule. A semi-natural testing of the developed algorithm was carried out and its effectiveness in solving tasks not only of automatic tracking of objects, but also in tasks of automatic objects detection using several reference images were evaluated. In conclusion, the authors present steps for further improving the accuracy and performance of the developed algo-rithm for its forthcoming implementation for on-board computing systems of aerial vehicles.

• CONTROL OF A MULTI-ROBOT SYSTEM BASED ON HIGHER-ORDER SLIDING MODES

  Nandanwar Anuj , L. А. Rybak , D. А. Dyakonov

  72-83

  2025-11-10

  Abstract ▼

  The article addresses the control problem of a second-order multi-agent robotic system with discrete time under network-induced delays. A novel approach to formation control is proposed, based on higher-order sliding mode control and cloud technologies. The interaction between agents is described using graph theory, where the Laplacian matrix  represents the communication channel between agents and the leader. The system dynamics are modeled by motion equations for the position and velocity of each agent. Special attention is paid to the impact of network-induced delays that occur during data transmission from sensors to the controller and from the controller to actuators. A multi-stage state predictor is developed, utilizing prediction methods to compensate for random delays in the network.
  The proposed control algorithm ensures rapid convergence of the system to the desired formation even in the presence of significant network delays. For each agent, a sliding surface and a reaching law are defined, taking into account multiple timestamps. A detailed stability analysis of the closed-loop system confirms the asymptotic stability of the developed control algorithm. Simulation results in MATLAB demonstrate the high efficiency of the proposed approach: a system consisting of five followers and one leader achieves the desired formation in 10.3 seconds and successfully maintains it despite random network delays. Compared to traditional first-order control methods, the new approach shows significantly improved performance, particularly in reducing chattering effects in control signals. The use of cloud technologies enables efficient real-time processing of large data volumes and implementation of complex prediction algorithms without overloading the local computational resources of the agents. The obtained results confirm the potential of the proposed approach for controlling multi-agent systems under real-world network constraints. The work also demonstrates the feasibility of using prediction methods to compensate for random packet losses and communication delays, ensuring reliable control and communication in dynamic, unpredictable scenarios