Balancing task allocation in multi-robot systems using K-means clustering and auction based mechanisms

Research highlights? We model multi-robot task allocation problem which maximize utilization and minimize the travel cost. ? We propose a methodology for the real time (NP Hard) multi-robot task allocation problem which includes clustering and auction based mechanism. ? Our methodology reduces computational complexity and provides better solution quality. ? Our analysis suggests practitioners to select appropriate number of task clusters based on silhouette values and balancing ratio. This paper aims to solve the balanced multi-robot task allocation problem. Multi-robot systems are becoming more and more significant in industrial, commercial and scientific applications. Effectively allocating tasks to multi-robots i.e. utilizing all robots in a cost effective manner becomes a tedious process. The current attempts made by the researchers concentrate only on minimizing the distance between the robots and the tasks, and not much importance is given to the balancing of work loads among robots. It is also found from the literature that the multi-robot system is analogous to Multiple Travelling Salesman Problem (MTSP). This paper attempts to develop mechanism to address the above two issues with objective of minimizing the distance travelled by 'm' robots and balancing the work load between 'm' robots equally. The proposed approach has two fold, first develops a mathematical model for balanced multi-robot task allocation problem, and secondly proposes a methodology to solve the model in three stages. Stage I groups the 'N' tasks into 'n' clusters of tasks using K-means clustering technique with the objective of minimizing the distance between the tasks, stage II calculates the travel cost of robot and clusters combination, stage III allocates the robot to the clusters in order to utilise all robot in a cost effective manner.