Genetic algorithms for gait synthesis in a hexapod robot

This paper describes the staged evolution of a complex motor pattern generator (CPG) for the control of the leg movements of a six-legged walking robot. The CPG is composed of a network of neurons. In contrast to the main stream work in neural networks, the interconnection weights are altered by a Genetic Algo-rithm (GA), rather than a learning algorithm. Staged evolution is used to improve the convergence rate of the algorithm, thus obtaining rapid evolution of behavior toward a goal set. First, an oscillator for the individual leg movements is evolved. Then, a network of these oscillators is evolved to coordinate the movements of the different legs. In this way, the designer specifies "islands of fitness " on the way to the final goal, rather than using a single fitness function or determining the ex-plicit solution to the control problem. By introducing a staged set of manageable challenges, the algorithm's performance is improved. These techniques may be applicable to other complex or ill-posed control prob-lems in robot control. The system itself determined how to evolve from one island to the next through the GA. 1