Traveling cellsman: Partition-cluster co-parameterization for multi-robot cooperative 3D printing

Abstract

We present Traveling Cellsman, an approach for creating a parameterization for task scheduling and collision avoidance with Cooperative 3D printing (C3DP). The parameterization is based on the distribution of work between robots (partition), which allows the robots to navigate through their printing tasks effectively while also allowing for collision avoidance with other robots. The parameterization provides straightforward optimization of makespan. Inspired by the multiple traveling salesman problem (MTSP), we schedule tasks by first clustering tasks together based on a parameterization of the partition. The clustered tasks can then be ordered for printing. Numerical results indicate that our clustering approach finds an optimal solution faster than the non-clustered approach for minimizing the pause and movement time of the robots. Physical results also show that optimization allows for faster printing time as compared to non-optimized or slicer-based methods for generating a printing schedule. While we demonstrate our method using C3DP, it is generally applicable to other multi-robot task scheduling problems where collision may occur.