Integration of Ant Colony Algorithm and Neural Networks for Task Management and Allocation in Groups of Mobile Robots

Abstract

This paper addresses the use of intelligent algorithms for task allocation and trajectory planning in groups of mobile robots. Structural schemes and methodologies for management and planning have been developed, where the ant colony algorithm is used for generating initial solutions for task distribution and route searching, while a neural network is applied for subsequent optimization and adjustment of robot trajectories in real-time. The overall structural scheme of the management and planning system includes elements such as a task distribution unit among robots, an initial solution generator, a trajectory optimizer, a robot control subsystem, and a monitoring and adjustment unit. Special attention is given to the implementation of the task distribution subsystem (initial solution generator) based on the ant colony algorithm and the movement trajectory optimization block based on a neural network. The initial solution generator is used to find the initial set of routes and task distribution strategies among a group of mobile robots using the ant colony algorithm, which simulates the foraging behavior of ants. The neural network analyzes the formed set of routes for efficiency and safety, adjusting them according to current conditions and new information about obstacles or changes in tasks. Simulation results are presented using a group of mobile robots working in a field with various obstacle configurations. The simulation results conclude the feasibility of using the developed approach in real-world task allocation scenarios in a group of robots.