An Auction-Based Task Allocation Algorithm in Heterogeneous Multi-Robot System

Today, robots are facing dynamic, real-time, complex adversarial, and stochastic work environment. It’s of great significance to research on task allocation problem in multi-robot system. In this paper, we propose a dynamic auction method for differentiated tasks under cost rigidities (DAMCR) which can find the optimal result in a static auction between robots and tasks. Then we analyze the optimality of DAMCR. Considering the dynamics of the system, we propose a partition-based task allocation adjustment method via distributed approach. To verify the effectiveness of the proposed scheme, we compare it with other task allocation methods based on classic Hungarian algorithm. In the experiments, we analyze the impact of the number of tasks and robots on the running time and robustness of the methods. The results suggest that our solution outperforms others, that is, robots can accomplish tasks faster and more effectively.