Heterogeneous Self-Organizing Map for Multi-type Tasks Allocation with Multirobot in Different Task Modes

Multirobot task allocation (MRTA) has been extensively utilized on a number of occasions along with the development in robotics system. In this paper, we focus on the multi-type tasks allocation with the proposed heterogeneous self-organizing map (SOM) algorithm. To endow the robots with excellent discernibility, the concept of pheromones between different type of tasks and robots are introduced. Also, the winner neuron selection criterion is based on the proposed robot utility according to different prescriptive task mode. The weight updating mechanism guides the robots to the specified task points. To validate the universality and practicality of the algorithm, the experiments were conducted in three different cases and a dynamic environment, the results show that the proposed heterogeneous SOM can adjust the mechanism of the multi-type MRTA to different situations, performing outstanding scalability.