Robot design for space missions using evolutionary computation

In this work, we describe a learning system that uses the CMA-ES method from evolutionary computation to optimize the morphology and the walking patterns for a complex legged robot simultaneously. Using simulation tools has the advantage that an optimization of robot morphology is possible before actually building the robot. Also, manually developing walking patterns for kinematically complex robots can be a challenging and time-consuming task. Both, the walking pattern and the morphology depend highly on each other to produce an energy-efficient and stable locomotion behaviour. In order to automate this design process, a learning system that generates, tests, and optimizes different walking patterns and morphologies is needed, as well as the ability to accurately simulate a robot and its environment. The evolutionary algorithm optimizes parameters that affect the trajectories of the robot´s foot points, testing the resulting walking patterns in a physical simulation. The robot´s limbs are controlled using inverse kinematics. In the future, the best solution evolved by this approach will be used for the mechanical construction of the real robot. Afterwards, the optimized walking patterns will be transferred to the real robot.