Evolutionary co-optimization of control and system parameters for a resonating robot arm

In this paper we simultaneously optimize the parameters describing the morphology of a robot arm and the parameters of its nonlinear controller. A novel concept of a pick-and-place robot arm is considered, which is called the resonating arm (RA). It uses a nonlinear spring mechanism to generate pick-and-place motions without the need for powerful actuators. This improves energy efficiency, cost and weight of the robot arm. Because of the complex interactions of the spring mechanism and the controller, we use evolutionary co-optimization to optimize the RA system as a whole. The results reveal that evolutionary co-optimization yields near optimal solutions for a 1 degree of freedom (1-DOF) RA, which require 43% less torque than the solution found through a separate optimization of the system and the control parameters. In case of a 2-DOF RA, evolutionary co-optimization resulted in credible solutions as well, but with less consistency.