Task assignment of multi-robot systems based on improved genetic algorithms

Abstract

Task assignment plays a significant role in achieving high utilization of robots and completing some complicated tasks in multi-robot systems. In this paper, an improved genetic algorithm (IGA) is presented to solve the task assignment problem of multi-robot systems in which n robots are used to search and recon a given area quickly and safely. To solve this problem, the given area is divided into many same subareas and searching each subarea is a subtask. In IGA, an appropriate fitness function and some improved genetic operators are proposed based on previous genetic algorithms (GAs), which have the advantages of avoiding local optimum and inhibiting premature. In addition, parallel processing structures are applied in IGA to reduce the time of finding the optimal solution. Some experiments are conducted and the results show that the proposed IGA has better performance than traditional GAs and ant colony optimization (ACO) for the task assignment problems.