OPTIMIZATION OF SWARM ROBOTICS ALGORITHMS

Context. Among the variety of tasks solved by robotics, one can single out a number of those for the solution of which small dimensions of work are desirable and sometimes necessary. To solve such problems, micro-robots with small dimensions are needed, the mass of which allows them to move freely in tight passages, in difficult weather conditions, and remain unnoticed. At the same time, the small dimensions of the microrobot also impose some indirect restrictions; therefore, it is better to use groups of microrobots for the solution of these problems. The efficiency of using groups of microrobots depends on the chosen control strategy and stochastic search algorithms for optimizing the control of a group (swarm) of microrobots. Objective. The purpose of this work is to consider a group of swarm algorithms (methods) belonging to the class of metaheuristics. The group of these algorithms includes, in particular, the ant colony algorithm, the possibilities of which were investigated to solve the traveling salesman problem, which often arises when developing an algorithm for the behavior of a group of microrobots. Method. At the first stage of the study, the main groups of parameters were identified that determine the flow and characterize the state at any time of the ant colony algorithm: input, control, disturbance parameters, output parameters. After identifying the main groups of parameters, an algorithm was developed, the advantage of which lies in scalability, as well as guaranteed convergence, which makes it possible to obtain an optimal solution regardless of the dimension of the graph. At the second stage, an algorithm was developed, the code of which was implemented in the Matlab language. Computer experiments were carried out to determine the influence of input, control, output, and disturbance parameters on the convergence of the algorithm. Attention was paid to the main groups of indicators that determine the direction of the method and characterize the state of the swarm of microrobots at a given time. In the computational experiment, the number of ants placed in the nodes of the network, the amount of pheromone, the number ofgraph nodes were varied, the number of iterations to find the shortest path, and the execution time of the method were determined. The final test of modeling and performance of the method was carried out. Results. Research has been carried out on the application of the ant algorithm for solving the traveling salesman problem for test graphs with a random arrangement of vertices; for a constant number of vertices and a change in the number of ants, for a constant number of vertices at different values of the coefficient Q; to solve the traveling salesman problem for a constant number of vertices at different values of the pheromone evaporation coefficient p; for a different number of graph vertices. The results showed that ant methods find good traveling salesman routes much faster than clear-cut combinatorial optimization methods. The dependence of the search time and the found optimal route on the values of control parameters are established using the example of test networks for a different number of graph vertices and iterations. Conclusions. The studies were carried out to make it possible to give recommendations on the application of the ant colony algorithm to control a group (swarm) of microrobots.