Application Of Artificial Neural Network And Immune-Genetic Algorithm With Elitist To Cooperative Transport Of Multi-robots System

The mechanism of the multi-agent system's control has been an interesting research project in the fields of robotics and artificial intelligence. It incorporates many important subjects, such as intelligent control, artificial life, evolutionary computation, robot programming technology, etc. With the development of research on multi-agent system, it has become a trend to conduct the optimization design and performance simulation of multi-agent system research based on the artificial life as well as evolutionary algorithms.In this paper, we have developed an innovative control strategy, based on the evolvable artificial neural network and the artificial immune algorithm with elitism for the cooperation behaves of the Multi-Robot Systems. Using this system we executed performance simulation and parametric design of the cooperative transport behavior for Multi-Robot Systems with single prey. The innovative Immune-Genetic Algorithm with elitist gives the new definitions of parameters for algorithm. This system used a Back Propagation Neural Network with three layers to process the perceptive information of every robot and made a decision for its action. The IGAE was used to optimize the connection weight values of this neural network, which made the performance of the neural network to be evolved continuously and finally a behavior decision-making system with good performance could be obtained.The results of simulation experiments indicate that the mobile robots can keep stable transport of the prey, which proves the good decision-making ability of the IGAE-ANN behavior decision-making system during the transport.