Uav – virtual migration based on obstacle avoidance model

Ci Fong He, Chin Feng Lai, Shau Yin Tseng, Ying Hsun Lai

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


In recent years, the obstacles avoidance technology of unmanned aerial vehicles has been developed rapidly. It takes a lot of manpower to control un-manned aerial vehicles, so many researches use reinforcement learning to make unmanned aerial vehicles fly autonomously. In the real environment using rein-for cement learning to train aircraft is an expensive and time-consuming work, because reinforcement learning is a way to learn from mistakes, so there are often bumps in the learning process. In Wu’s research, they trained a good model, but the realistic environment and simulation environment differs very big, so we will train this model again and transferred to the real environment, makes unmanned aerial vehicle in the realistic environment can use cheaper and quickly achieve the same task.

Original languageEnglish
Title of host publicationCognitive Cities - 2nd International Conference, IC3 2019, Revised Selected Papers
EditorsJian Shen, Yao-Chung Chang, Yu-Sheng Su, Hiroaki Ogata
Number of pages8
ISBN (Print)9789811561122
Publication statusPublished - 2020
Event2nd International Cognitive Cities Conference, IC3 2019 - Kyoto, Japan
Duration: 2019 Sep 32019 Sep 6

Publication series

NameCommunications in Computer and Information Science
Volume1227 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference2nd International Cognitive Cities Conference, IC3 2019

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

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