A1 Journal article (refereed), original research (Journal article, original research)

Machine Learning Model for a Dynamic Path Planning Problem


Open Access publication


Publication Details

Authors: Huu Thong Tran, Duc Duy Nguyen, Nananukul Narameth

Publisher: IOP Publishing: Conference Series

Publication year: 2020

Language: English

Related journal or series: Journal of Physics: Conference Series

Volume number: 1624

ISSN: 1742-6588

eISSN: 1742-6596

JUFO level of this publication: 1

Digital Object Identifier (DOI): http://dx.doi.org/10.1088/1742-6596/1624/2/022031

Open Access: Open Access publication


Abstract

Due to an advancement in Industry 4.0 technology, various autonomous systems have been developed in order to increase the operational efficiency. This paper considers an application of Industry 4.0 technology to an autonomous transportation operation. The paper focuses on applying a machine learning technique to a dynamic path planning problem where real-time randomized obstacle locations are considered. The routes or the solutions from the dynamic path planning problem are determined by an A-star algorithm, which are then used to build machine learning models based on an artificial neural network. The models were developed to discover the relationship between the input and output of the dynamic path planning problem. The structure of the network which is defined by the number of intermediate layers and the number of nodes is provided, where the overall accuracy is used to evaluate the setting efficiency. The proposed methodology was tested with a problem that consists of 7 types of paths, and the number of randomized obstacles fluctuated from 1 to 8. The paths were generated based on a layout of a consumer product warehouse. The proposed model succeeded in predicting the robot paths with 98.5% prediction accuracy.


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Last updated on 2021-07-12 at 13:43