An evolutionary method of adaptive behavior for robot based on echo state network

For the re-evolution of the mobile robot behavior in unknown environments, the mapping relation was constructed between input of sensors and output of actuators based on echo state network. An algorithm of adaptive behavior learning was presented based on echo state network for evolutionary robotics. The composite architecture with responsive behavior and behavior learning was adopted. The responsive behavior was drived by the samples composed with sensor information and decision. The weights of echo state network were optimized via (μ+λ)-evolution strategy. The new control rules were generated via evolutionary algorithms, and new samples were added to the database constantly. The high intelligent behaviors of robot were transmitted to responsive behaviors. The experimental results indicate that the proposed approach has a better adaptability.