Evolving Gaits for the Lynxmotion Hexapod II Robot

Gait generation for hexapod robots can be accomplished with a two-phase process, whereby leg cycles that effectively provide thrust are learned and then the proper selection and coordination of these cycles needed to produce a gait are learned. Genetic algorithms can be used to learn both the leg cycles and the coordination. In previous work, this technique was used successfully to generate gaits for the ServoBot. In this paper, we apply the techniques that were successful with the ServoBot to the Lynxmotion Hexapod II Robot. Although physical differences between the two robots required changes to the robot simulations, genetic algorithms and the two-phase successfully learned effective gaits.