Abstract
In an effort to solve to the problems of dynamic relative mental, learning ability and moving time optimisation in the cooperation of multi-robots, a layer-dividing task cooperation model is proposed. Firstly, the changing features of the mental layer in a dynamic environment is shown through the belief, target, intention and knowledge. Secondly, the strategy learning and task planning under the condition of maximum united rewards are constructed by combining the reinforcement learning and particle swarm optimisation. Thirdly, the robot's fast moving method is designed based on the basic movement states and Bezier curve track. The validity of the model is proved through experiments.
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