Результаты поиска по 'robotics':
Найдено статей: 23
  1. Vasiliev A.N., Karp V.P.
    Modeling self-regulation of active neuron in the network
    Computer Research and Modeling, 2012, v. 4, no. 3, pp. 613-619

    A model of the behavior of the active neuron, which was the development of the model described in Shamis A.L. [Shamis, 2006], is designed. Proposed topology is locally connected matrix of the active neural network and the structure integration of information from different sources. An example of the script behavior robot controlled by this neural network is described. The results of experiments with the software implementation of a neural network are presented.

    Keywords: neural network, neuron, recognition.
    Views (last year): 1.
  2. Vetchanin E.V., Tenenev V.A., Shaura A.S.
    Motion control of a rigid body in viscous fluid
    Computer Research and Modeling, 2013, v. 5, no. 4, pp. 659-675

    We consider the optimal motion control problem for a mobile device with an external rigid shell moving along a prescribed trajectory in a viscous fluid. The mobile robot under consideration possesses the property of self-locomotion. Self-locomotion is implemented due to back-and-forth motion of an internal material point. The optimal motion control is based on the Sugeno fuzzy inference system. An approach based on constructing decision trees using the genetic algorithm for structural and parametric synthesis has been proposed to obtain the base of fuzzy rules.

    Keywords: optimal motion control, self-locomotion, genetic algorithm, structural-parametrical synthesis, decision tree, fuzzy logic.
    Views (last year): 2. Citations: 1 (RSCI).
  3. Bratsun D.A., Kostarev K.V.
    Mathematical modeling of phase transitions during collective interaction of agents in a common thermal field
    Computer Research and Modeling, 2025, v. 17, no. 5, pp. 1005-1028

    Collective behavior can serve as a mechanism of thermoregulation and play a key role in the joint survival of a group of organisms. In higher animals, such phenomena are usually the subject of study of biology since sudden transitions to collective behavior are difficult to differentiate from the psychological and social adaptation of animals. However, in this paper, we indicate several important examples when a flock of higher animals demonstrates phase transitions similar to known phenomena in liquids and gases. This issue can also be studied experimentally within the framework of synthetic systems consisting of self-propelled robots that act according to a certain given algorithm. Generalizing both of these cases, we consider the problem of phase transitions in a dense group of interacting selfpropelled agents. Within the framework of microscopic theory, we propose a mathematical model of the phenomenon, in which agents are represented as bodies interacting with each other in accordance with an effective potential of a special type, expressing the desire of agents to move in the direction of the gradient of the joint thermal field. We show that the number of agents in the group, the group power, is the control parameter of the problem. A discrete model with individual dynamics of agents reproduces most of the phenomena observed both in natural flocks of higher animals engaged in collective thermoregulation and in synthetic complex systems. A first-order phase transition is observed, which symbolizes a change in the aggregate state in a group of agents. One observes the self-assembly of the initial weakly structured mass of agents into dense quasi-crystalline structures. We demonstrate also that, with an increase in the group power, a second-order phase transition in the form of thermal convection can occur. It manifests in a sudden liquefaction of the group and a transition to vortex motion, which ensures more efficient energy consumption in the case of a synthetic system of interacting robots and the collective survival of all individuals in the case of natural animal flocks.With an increase in the group power, secondary bifurcations occur, the vortex structure in agent medium becomes more complicated.

    Keywords: agent-based modeling, self-organization, bioconvection.
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