Optimizing performance in automation through modular robots

Flexible manufacturing and automation require robots that can be adapted to changing tasks. We propose to use modular robots that are customized from given modules for a specific task. This work presents an algorithm for proposing a module composition that is optimal with respect to performance metrics such as cycle time and energy efficiency, while considering kinematic, dynamic, and obstacle constraints. Tasks are defined as trajectories in Cartesian space, as a list of poses for the robot to reach as fast as possible, or as dexterity in a desired workspace. In a simulated comparison with commercially available industrial robots, we demonstrate the superiority of our approach in randomly generated tasks with respect to the chosen performance metrics. We use our modular robot proModular.1 for the comparison.