Navigating assistance system for quadcopter with deep reinforcement learning

Tung Cheng Wu, Shau Yin Tseng, Chin Feng Lai, Chia Yu Ho, Ying Hsun Lai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In this paper, we present a deep reinforcement learning method for quadcopter bypassing the obstacle on the flying path. In the past study, algorithm only control the forward direction about quadcopter. In this letter, we use two function to control quadcopter. One is quadcopter navigating function. It is based on calculating coordination point and find the straight path to goal. The other function is collision avoidance function. It is implemented by deep Q-network model. Both two function will output rotating degree, agent will combine both output and turn direct. Besides, deep Q-network can also make quadcopter fly up and down to bypass the obstacle and arrive at goal. Our experimental result shows that collision rate is 14% after 500 flights. Based on this work, we will train more complex sense and transfer model to real quadcopter.

Original languageEnglish
Title of host publicationProceedings - 2018 1st International Cognitive Cities Conference, IC3 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages16-19
Number of pages4
ISBN (Electronic)9781538650592
DOIs
Publication statusPublished - 2018 Dec 6
Event1st International Cognitive Cities Conference, IC3 2018 - Okinawa, Japan
Duration: 2018 Aug 72018 Aug 9

Publication series

NameProceedings - 2018 1st International Cognitive Cities Conference, IC3 2018

Other

Other1st International Cognitive Cities Conference, IC3 2018
CountryJapan
CityOkinawa
Period18-08-0718-08-09

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Education
  • Urban Studies

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  • Cite this

    Wu, T. C., Tseng, S. Y., Lai, C. F., Ho, C. Y., & Lai, Y. H. (2018). Navigating assistance system for quadcopter with deep reinforcement learning. In Proceedings - 2018 1st International Cognitive Cities Conference, IC3 2018 (pp. 16-19). [8567159] (Proceedings - 2018 1st International Cognitive Cities Conference, IC3 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IC3.2018.00013