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Updated: Oct 5, 2025
07:49
Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
Published on: August 16, 2017
7.2K
Robust Assembly Assistance Using Informed Tree Search with Markov Chains.
Arpad Gellert1, Radu Sorostinean1,2, Bogdan-Constantin Pirvu3
1Computer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, 550025 Sibiu, Romania.
Sensors (Basel, Switzerland)
|January 22, 2022
Summary
This study introduces a hybrid algorithm combining Markov chains and informed tree search for predicting assembly steps. This method enhances training for manual manufacturing operations, especially in novel situations.
Area of Science:
- Manufacturing Engineering
- Human-Computer Interaction
- Artificial Intelligence
Background:
- Manual labor is a significant segment of the European manufacturing sector.
- Enhancing training for manual operations offers competitive advantages.
- Current training methods may lack scalability and adaptability.
Purpose of the Study:
- To develop an innovative assembly training system for manual operations.
- To provide automated suggestions for the next assembly step.
- To support both inexperienced and experienced workers without on-site trainers.
Main Methods:
- A hybrid algorithm combining Markov chains and informed tree search was developed.
- The algorithm was evaluated using data from 179 participants (111 factory workers, 68 students).
- Prediction methods were compared for their efficacy in suggesting assembly steps.
Main Results:
- Markov chains alone were insufficient for new scenarios.
- The informed tree search component significantly improved prediction accuracy in unseen situations.
- The hybrid algorithm demonstrated superior efficiency and robustness compared to other predictors.
Conclusions:
- The proposed hybrid algorithm is highly suitable for adaptive assembly support systems.
- This approach enhances training for manual operations in the manufacturing industry.
- The system offers robust solutions for next assembly step prediction in dynamic environments.
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