Beyond Feasibility: Efficiently Planning Robotic Assembly Sequences That Minimize Assembly Path Lengths

Abstract

Advancements in Industry 4.0 demand sophisticated solutions for automatic robotic assembly sequence planning (RASP), capable of handling the diversity and complexity of modern manufacturing tasks. One approach to RASP is Assembly-by-Disassembly (AbD). It first searches for a disassembly sequence that is then inverted to obtain an assembly sequence. One of the challenges of AbD, however, is the exponential number of potential assembly sequences for any given assembly. To mitigate this challenge, we propose to transfer knowledge obtained during previous planning attempts. Specifically, we present an approach that combines Monte Carlo Tree Search (MCTS) with deep Q-learning to optimize the total length of robotic assembly paths. We use a graph-based representation of disassembly states in combination with a graph neural network to learn the Q-function. We further discuss a principled approach to generate 3D assemblies out of aluminium profiles that a single robot manipulator can assemble. With this approach, we generated two datasets consisting of 14 assemblies with 21 removable parts and 7 assemblies with 30 removable parts. Using leave-one-out cross-validation, we were able to demonstrate how our approach outperformed an unmodified MCTS. Moreover, we successfully transferred knowledge between datasets.