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DOI: 10.14569/IJACSA.2022.01312122
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Research on Key Technologies of Smart City Building Interior Decoration Construction based on In-Depth Learning

Author 1: Li Zhang
Author 2: Aimin Qin

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 12, 2022.

  • Abstract and Keywords
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Abstract: The intelligentization of building interior decoration construction is of great significance to the construction of smart city, and robot automation has brought an opportunity for this. Robot self-decoration is the development trend in the future. One of the key issues involved, is the self-planning of mobile path. In this regard, the research adopts the proximal policy optimization algorithms (PPO) to improve the self-planning path ability of the decoration robot. For the information of lidar and robot status, the Full Connect Neural Network (FCNN) is used to process it. In addition, the reward function and the corresponding Credit Assignment Problem (CAP) model are designed, to accelerate the learning process of path planning. Aiming at the dynamic uncertainty in the actual environment, the adaptive loss function is used to build an auxiliary model to predict the environmental change. The simulation results show that the research and design strategy significantly improves the learning efficiency and path planning success rate of the decoration robot, and shows good adaptability to the dynamic environment, which has important reference significance for the practical application of the decoration robot.

Keywords: Interior decoration; path planning; deep reinforcement learning; reward function; credit allocation

Li Zhang and Aimin Qin, “Research on Key Technologies of Smart City Building Interior Decoration Construction based on In-Depth Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 13(12), 2022. http://dx.doi.org/10.14569/IJACSA.2022.01312122

@article{Zhang2022,
title = {Research on Key Technologies of Smart City Building Interior Decoration Construction based on In-Depth Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.01312122},
url = {http://dx.doi.org/10.14569/IJACSA.2022.01312122},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {12},
author = {Li Zhang and Aimin Qin}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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