MULTI-ROBOT TASK ALLOCATION FOR FIRE-DISASTER RESPONSE BASED ON REINFORCEMENT LEARNING

摘要

In order to achieve distributed task allocation dynamically and efficiently for multi-robot systems, multi-robot fire-disaster response was presented, because of its dynamic characteristic. The proposed multi-robot task allocation algorithm for fire-disaster response is based on reinforcement learning. The reinforcement learning algorithm for multi-robot is divided into two types: non-cooperation and cooperation, this algorithm satisfies the requirement of dynamic task allocation for fire-disaster response. The experimental results verify that the proposed strategy can achieve efficient multi-robot dynamic task allocation for fire-disaster response, and the fires are extinguished timely.