Evolutionary cost-optimal composition synthesis of modular robots considering a given task

Commercially available robots cannot always be adapted to arbitrary tasks or environments, particularly when the task would exceed the kinematic or dynamic limits of the robot. Modular robots offer a solution to this problem, since they can be reconfigured in various ways from a set of modules. The challenge of choosing the optimal composition for a given task, however, is hard since the search space of compositions is vast. Our approach addresses this problem: instead of finding the cost-optimal solution over all possible compositions individually, we propose a time-efficient composition synthesis method which uses evolutionary algorithms by taking task-related objectives into account. Simulations show that our algorithm finds the cost-optimal module composition with less computation time than other methods in the literature.