A Study on Generating Good Environment Patterns for Evolving Robot Navigators

To evolve robot navigators that generalize well, we should evaluate the navigators using as many environment patterns as possible during evolution. To reduce the computational cost, however, we should use as few environment patterns as possible. It is difficult to know in advance what patterns can evolve good navigators. To solve this problem, we study two different approaches. One is the co-evolutionary algorithm (CEA) that evolves the navigators and the environment patterns together, and the other is to select good environment patterns through incremental evolution (IE). Detailed considerations in using CEA and IE are described in this paper, and their efficiency is compared with each other through simulations.