Robust Assembly Assistance Using Informed Tree Search with Markov Chains.

Arpad Gellert,Bogdan-Constantin Pirvu,Radu Sorostinean

doi:10.3390/s22020495

Arpad Gellert, Bogdan-Constantin Pirvu + Show 1 more

Open Access

https://doi.org/10.3390/s22020495

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Abstract

Manual work accounts for one of the largest workgroups in the European manufacturing sector, and improving the training capacity, quality, and speed brings significant competitive benefits to companies. In this context, this paper presents an informed tree search on top of a Markov chain that suggests possible next assembly steps as a key component of an innovative assembly training station for manual operations. The goal of the next step suggestions is to provide support to inexperienced workers or to assist experienced workers by providing choices for the next assembly step in an automated manner without the involvement of a human trainer on site. Data stemming from 179 experiment participants, 111 factory workers, and 68 students, were used to evaluate different prediction methods. From our analysis, Markov chains fail in new scenarios and, therefore, by using an informed tree search to predict the possible next assembly step in such situations, the prediction capability of the hybrid algorithm increases significantly while providing robust solutions to unseen scenarios. The proposed method proved to be the most efficient for next assembly step prediction among all the evaluated predictors and, thus, the most suitable method for an adaptive assembly support system such as for manual operations in industry.

Highlights

To compete successfully in the global market, in recent years, factories have turned their attention to optimize all tasks, including the ones performed by humans, leveraging the progress in information technology with the deployment of artificial intelligence [1,2]and machine learning [3] in various application areas throughout the product life cycle.Industry 4.0 [4] is the coined term used to describe this optimization involving interconnection and collaboration among the factory’s interactants towards a human–automation symbiosis.Nowadays, the adoption process in Industry 4.0 mainly focuses on assisting humans with different technologies in order to ease their tasks and improve productivity, as envisioned in the Operator 4.0 concept [5]
A new method based on an informed tree search on top of Markov chain prediction was applied to suggest a plausible move for the assembly step
When the Markov chain fails in its prediction due to a new scenario, an informed tree search is performed

Summary

Introduction

Industry 4.0 [4] is the coined term used to describe this optimization involving interconnection and collaboration among the factory’s interactants (human and synthetic) towards a human–automation symbiosis. The adoption process in Industry 4.0 mainly focuses on assisting humans with different technologies in order to ease their tasks and improve productivity, as envisioned in the Operator 4.0 concept [5]. This is because full automation is costly and not effective in all areas due to dexterity, flexibility, and complexity requirements. Assistance systems are required to facilitate interconnection and collaboration between humans and synthetic systems, especially in the case of manual work. Recent assembly assistance systems target multi-modal interaction with the user and are capable of providing adaptive instructions (i.e., type, content, sequence) tailored for different user categories (e.g., experience, age, gender) that take into account the user’s basic emotion and/or mental state

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