Generating comprehensible moving policies for mobile robots through co-evolution of navigators and environment patterns

To evolve robot navigators that generalize well, we should evaluate the navigators using as many environment patterns (EPs) as possible during evolution. On the other hand, to reduce the computational cost, we should consider as less EPs as possible. A good compromise is to use some well selected EPs. However, selection of good EPs is in general a very hard problem. To solve this problem, we have proposed an improved co-evolutionary algorithm (ICEA) for evolving the navigators and the EPs together. So far, we have verified the ICEA with neural network (NN) navigators. The NN navigators, however, are black-boxes that cannot be understood easily. In this paper, we try to evolve fuzzy rule (FR) navigators with the ICEA. Simulation results show that the FR navigators evolved by using ICEA generalize as well as the NN navigators, and they are much more comprehensible.