title: ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð´ÐµÑÐµÐ½ÑÑÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½Ð¾Ð³Ð¾ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð³ÑÑÐ¿Ð¿Ð¸ÑÐ¾Ð²ÐºÐ¾Ð¹ ÑÐ¾Ð±Ð¾ÑÐ¾Ð² Ð¿ÑÐ¸ Ð»Ð¸ÐºÐ²Ð¸Ð´Ð°ÑÐ¸Ð¸ ÑÐµÑ Ð½Ð¾Ð³ÐµÐ½Ð½ÑÑ Ð°Ð²Ð°ÑÐ¸Ð¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
Creators: name: ÐÑÑÐ°Ð¼ÐµÐ´ÑÐ¸Ð½, ÐÐ³Ð¾ÑÑ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: Ð³ÑÑÐ¿Ð¿Ð° ÑÐ¾Ð±Ð¾ÑÐ¾Ð²
Ð»Ð¸ÐºÐ²Ð¸Ð´Ð°ÑÐ¸Ñ Ð°Ð²Ð°ÑÐ¸Ð¹
Ð´ÐµÑÐµÐ½ÑÑÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½Ð¾Ðµ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ðµ
Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ñ Ð¿Ð¾Ð´ÐºÑÐµÐ¿Ð»ÐµÐ½Ð¸ÐµÐ¼
Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
Ð¿Ð»Ð°Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð´ÐµÐ¹ÑÑÐ²Ð¸Ð¹
ÑÐ°ÑÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ Ð·Ð°Ð´Ð°Ñ
group of robots
accident response
decentralized control
reinforcement learning
simulation
action planning
task distribution

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description: The object of the research is the methods of situational management. The subject of the research is methods of decentralized control of a group of robots for the elimination of technogenic accidents in urban infrastructure. The aim of the work is to develop an algorithm for making decisions to eliminate technogenic accidents at each moment of time when the flow of events changes for each robot in a heterogeneous group with a decentralized method of group control. A model of robot, a model of the environment, a model of an accident source, a model and an algorithm for changing the flow of events, algorithms for information exchanges between robots, an algorithm for planning actions of a heterogeneous group of robots based on an iterative procedure for optimizing a collective solution, a target function as a sum of efficiency functions multiplied by the corresponding strategic coefficients, an algorithm for constructing trajectories of movement of robots in a simulated environment were developed in this thesis work. A software implementation of the simulated environment was developed. The optimal values of the strategic coefficients of the efficiency functions for controlling a group of robots were found. It is experimentally shown that the efficiency functions reduce the average number of clock cycles required for the complete elimination of a technogenic accident for various parameters of the accident scenarios. Using the model-free policy gradient algorithm Proximal Policy Optimization, estimates of the time required for the complete elimination of a technogenic accident were obtained for various parameters of the accident scenarios. The obtained estimates differ by less than 5% from the estimates obtained using the created target function and the iterative procedure for optimizing the collective solution with the same parameters of the accident scenarios.