How to find assembly plans (fast): Hierarchical state space partitioning for efficient multi-robot assembly

Abstract

The programming of cooperating teams of robots in automation and in particular for assembling small batch sizes is a tedious task. Apart from that, the use of AI-based planning for multiple robots is computationally expensive. Using state space techniques, the state space increases dramatically for numerous possibilities in an indiscrete, continuous world. Hence, in this work we present a two-layer planning algorithm for multi-robot assembly which automatically partitions the state space in order to reduce complexity and to speed up planning time. We depict formal considerations about planning complexity and show how and why the state space size and, thus, planning time is strongly lower compared to common approaches.