An Improved Multi-Robot Task Allocation Algorithm Based on the Hungarian Algorithm

When it comes to Multi-Robot Systems (MRS), Multi-Robot System Task Allocation (MRTA) is one of the key issues in realizing multi-robot systems for real-world applications. In this paper, we propose an optimization-based Hungarian algorithm to solve the task allocation problem of multi-robot systems. Firstly, each robot is allowed to autonomously evaluate the task cost and send it to the central processor and an equilibrium compensation approach is proposed to compensate the constraints of the algorithm for the cost matrix to be square. Then a task cache pool is used to avoid the problem from degenerating into Single-Robot (SR) task allocation, and a prioritization strategy considering time is also designed to prevent the task points with lower generation values from lingering for a long time in the dynamic task allocation. Through simulation experiments, we demonstrate that the algorithm has a positive effect on the efficiency of assigned tasks in both dynamic and static situations.