A New Method for Improving the Fairness of Multi-Robot Task Allocation by Balancing the Distribution of Tasks

Abstract

This paper presents an innovative task allocation method for multi-robot systems that aims to optimize task distribution while taking into account various performance metrics such as efficiency, speed, and cost. Contrary to conventional approaches, the proposed method takes a comprehensive approach to initialization by integrating the K-means clustering algorithm, the Hungarian method for solving the assignment problem, and a genetic algorithm specifically adapted for Open Loop Travel Sales Man Problem (OLTSP). This synergistic combination allows for a more robust initialization, effectively grouping similar tasks and robots, and laying a strong foundation for the subsequent optimization process. The suggested method is flexible enough to handle a variety of situations, including Multi-Robot System (MRS) with robots that have unique capabilities and tasks of varying difficulty. The method provides a more adaptable and flexible solution than traditional algorithms, which might not be able to adequately address these variations because of the heterogeneity of the robots and the complexity of the tasks. Additionally, ensuring optimal task allocation is a key component of the suggested method. The method efficiently determines the best task assignments for robots through the use of a systematic optimization approach, thereby reducing the overall cost and time needed to complete all tasks. This contrasts with some existing methods that might not ensure optimality or might have limitations in their ability to handle a variety of scenarios. Extensive simulation experiments and numerical evaluations are carried out to validate the method's efficiency. The extensive validation process verifies the suggested approach's dependability and efficiency, giving confidence in its practical applicability.