Modification of the Hopfield Neural Network Model for Solving the Problem of Optimal Task Allocation in a Group of Mobile Robots

Abstract

In the context of group interaction among mobile robots, there arises the challenge of task distribution within the group, taking into consideration the robots' characteristics and the working environment. This study aims to modify the Hopfield neural network and develop methodologies for its application in solving the task allocation problem for an arbitrary number of tasks within a group of mobile robots. To achieve this, the Hopfield neural network is represented as a graph. An algorithm is presented, demonstrating the transition from the initial problem to the Traveling Salesman Problem (TSP). The application of the Hopfield model to the task distribution problem in a group of robots is described, together with the development of an optimization function calculation algorithm. An assessment is conducted to evaluate the impact of neural network parameters on the quality and speed of solving the optimization problem. By comparing it with other heuristic methods (genetic and ant colony algorithms (ACAs), the domains of application for the modified algorithm are determined.