Task Allocation in Emotional Robot Pursuit Based on Self-organizing Algorithm

A self-organizing algorithm based on self-organizing map (SOM) neural network is proposed and task allocation is integrated into the training process of the network. Taking advantages of the decisive effect of the competitive winning function value in network training (task allocation) and combining with the real-time performance of task allocation in the proposed algorithm, the emotional robot provides the competitive victory function value, which is adjusted by reinforcement-learning, according to the emotional factors. In this way, the emotion directly and effectively participates in task allocation decisions, and the algorithm performance is optimized. Finally, simulation experiments are carried out to verify the effectiveness of the proposed algorithm. Especially, with the scale of emotional robot team growing, the pursuit time can be shortened by more than 50% compared with the existing algorithm.