Holistic Optimization of Modular Robots

TL;DR

This paper presents a holistic optimization approach for modular robots, jointly optimizing their composition, base placement, and trajectories to significantly reduce cycle times and improve deployment efficiency in industrial tasks.

Contribution

It introduces the first comprehensive method to optimize all aspects of modular robot configuration simultaneously, outperforming previous module-only optimization approaches.

Findings

Up to 25% reduction in cycle time.

Feasible solutions found in twice as many benchmarks.

Successful real-world deployment in 9 out of 10 cases.

Abstract

Modular robots have the potential to revolutionize automation, as one can optimize their composition for any given task. However, finding optimal compositions is non-trivial. In addition, different compositions require different base positions and trajectories to fully use the potential of modular robots. We address this problem holistically for the first time by jointly optimizing the composition, base placement, and trajectory to minimize the cycle time of a given task. Our approach is evaluated on over 300 industrial benchmarks requiring point-to-point movements. Overall, we reduce cycle time by up to 25 % and find feasible solutions in twice as many benchmarks compared to optimizing the module composition alone. In the first real-world validation of modular robots optimized for point-to-point movement, we find that the optimized robot is successfully deployed in nine out of ten cases in less than an hour.