Analytical review of approaches to the distribution of tasks for mobile robot teams based on soft computing technologies

The use of various types of heuristic algorithms based on soft computing technologies for the distribution of tasks in groups of mobile robots performing monosyllabic operations in a single workspace is considered: genetic algorithms, ant algorithms and artificial neural networks. It is shown that this problem is NP-complex and its solution by direct iteration for a large number of tasks is impossible. The initial problem is reduced to typical NP-complete problems: the generalized problem of finding the optimal group of closed routes from one depot and the traveling salesman problem. A description of each of the selected algorithms and a comparison of their characteristics are presented. A step-by-step algorithm of operation is given, taking into account the selected genetic operators and their parameters for a given population volume. The general structure of the developed algorithm is presented, which makes it possible to solve a multi-criteria optimization problem efficiently enough, taking into account time costs and the integral criterion of robot efficiency, taking into account energy costs, functional saturation of each agent of the group, etc. The possibility of solving the initial problem using an ant algorithm and a generalized search for the optimal group of closed routes is shown. For multi-criteria optimization, the possibility of linear convolution of the obtained vector optimality criterion is shown by introducing additional parameters characterizing group control: the overall efficiency of the functioning of all robots, the energy costs for the functioning of the support group and the energy for placing one robot on the work field. To solve the task distribution problem using the Hopfield neural network, its representation is made in the form of a graph obtained during the transition from the generalized task of finding the optimal group of closed routes from one depot to the traveling salesman problem. The quality indicator is the total path traveled by each of the robots in the group.