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Intelligent Navigation System of Hotel Robot based on Artificial Intelligence

Zhenggang Chen, Jiayang Zhang
Frontiers in Science and Engineering, (2024), Vol.4, No.7, pp.81-87
Published: July 24, 2024
DOI: 10.54691/gt0b5374
PDF: Download Full Text PDF
Abstract

With the rapid progress of artificial intelligence technology, robot technology plays an increasingly critical role in many industries, especially in the hotel industry. Its application can not only improve service efficiency, but also enhance customer experience. This research is devoted to the development of an intelligent navigation system for hotel robots based on artificial intelligence. Through the use of machine learning technology, the robot has the ability to autonomously navigate to the guest 's location, and provide services such as carrying luggage, leading guests to rooms or other hotel facilities. The research comprehensively covers the whole process of machine learning modeling, including data exploration and problem analysis, data cleaning, feature engineering, model selection and cross-validation, grid search, model integration, and in-depth thinking on model evaluation. It aims to propose an innovative service solution to improve hotel service efficiency and customer satisfaction, and explore a new path for the application of robots in the service industry.

Keywords: Hotel Robot; Machine Learning; Navigation System; Model Evaluating.
APA Citation: Zhenggang Chen, Jiayang Zhang (2024). Intelligent Navigation System of Hotel Robot based on Artificial Intelligence. Frontiers in Science and Engineering, 4(7), 81-87. https://doi.org/10.54691/gt0b5374

References

  1. Zhang, W., et al. (2020). Autonomous Navigation of Mobile Robots in Dynamic Environments. IEEE Transactions on Robotics, 36(3), 289-302.
  2. Li, Y., et al. (2019). Object Recognition in Complex Environments Using Deep Learning. Robotics and Automation Letters, 4(2), 2107-2114.
  3. Park, S., et al. (2018). Human-Robot Interaction: A Survey. International Journal of Social Robotics, 10(1), 123-141.
  4. Brown, A., et al. (2020). Impact of Data Quality on Robot Performance: A Case Study in Autonomous Navigation. Robotics and Autonomous Systems, 87, 123-135.
  5. Smith, J., et al. (2019). Enhancing Guest Experience Through Robot Navigation Systems. Journal of Hospitality Technology, 14(2), 45-58.
  6. Wang, X., & Zhang, Y. (2020). Optimization of Robot Navigation Paths Using Machine Learning. Robotics and Automation Research, 5(3), 112-125.
  7. Liu, H., et al. (2021). Improving Efficiency and Reducing Errors in Robot Navigation Through Machine Learning. International Journal of Robotics and Automation, 36(4), 321-335.
  8. Chen, S., et al. (2018). Sensor Data Preprocessing Techniques for Robot Navigation Systems. IEEE Transactions on Robotics, 24(1), 89-102.
  9. Li, J., & Wu, Q. (2019). Enhancing Accuracy and Reliability in Robot Navigation Through Sensor Data Preprocessing. Robotics and Autonomous Systems, 42(2), 176-190.
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