A method of accelerating convergence for Genetic Algorithms evolving morphological and control parameters for a biomimetic robot

Abstract

In generating efficient gaits for biomimetic robots, control commands and robot morphology are closely coupled, particularly for soft bodied robots with complex internal dynamics. Achieving optimal robot energy consumption is only possible if robot control parameters and morphology are tuned simultaneously. Genetic Algorithms (GAs) are well suited for this purpose. In this application, however, GAs converge slowly because of the high dimensionality of the fitness landscape, the limited number of successful designs within this landscape, and the significant computational cost of evaluating the fitness function using dynamics simulations. To accelerate GA convergence for design applications involving biomimetic robots, a new physics-based preprocessing methodology is proposed. This preprocessing strategy was applied to develop gaits for a biomimetic caterpillar robot. Convergence speeds were observed to increase significantly through the application of the physics-based preprocessing.