Assembly Sequence Planning by Reinforcement Learning and Accessibility Checking using RRT*

Abstract

Automating Assembly Sequence Planning (ASP) is a core task in digitalizing industrial manufacturing. The objective of ASP is to find a feasible and efficient sequence, in which the components of a given assembly can be assembled. Often, the problem is tackled using an “assembly-by-disassembly” approach, i.e., a disassembly sequence is generated an then reversed to obtain an assembly sequence. Recently, approaches to ASP based on Reinforcement Learning (RL) and physics-based simulation environments have gained attention, wherein an RL- agent acts against the simulation environment to learn feasible disassembly sequences. We propose a combination of this approach with the path finding algorithm RRT<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> (Rapidly Exploring Random Tree *). The path finding algorithm generates lists of components that are in principle accessible at different stages of assembly, taking into account the space needed for the hand of the fitter or for a robotic arm. These lists are passed on to the RL agent, which then only attempts to manipulate and disassemble the currently accessible components. Using a validation assembly, we demonstrate that this approach can meaningfully improve the performance, compared to an unconstrained RL agent.