Title page for etd-0804118-233344


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URN etd-0804118-233344
Author YERRA PRATHYUSHA
Author's Email Address No Public.
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Department Computer Science and Engineering
Year 2017
Semester 2
Degree Master
Type of Document
Language English
Title UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization
Date of Defense 2018-07-30
Page Count 77
Keyword
  • Swarm intelligence
  • UAV
  • Particle Swarm Optimization (PSO)
  • Navigation algorithm
  • Path planning
  • Abstract The air pollution has become a major ecological issue. The surpassed pollution levels can be controlled by searching the pollution source. An environmental monitoring UAVs can address this issue. The challenge here is how UAVs collaboratively navigate towards pollution source under realistic pollution distribution. In this thesis, we proposed a novel methodology by using the collaborative intelligence learned from Golden shiners schooling fish. We adopted shiners collective intelligence with the particle swarm optimization (PSO). We used a Gaussian plume model for depicting the pollution distribution. Furthermore, our proposed method incorporates path planning and collision-avoidance for UAV group navigation.
    For path planning, we simulated obstacle rich 3D environment. The proposed methodology generates collision-free paths successfully. For group navigation of UAVs, the simulated environment includes a Gaussian plume model which considers several atmospheric constraints like temperature, wind speed, etc. The UAVs can successfully reach the pollution source with accuracy using the proposed methodology. Moreover, we can construct the unknown distribution by plotting the sensed pollution values by UAVs.
    Advisory Committee
  • Ming-Chao Chiang - chair
  • Chung-Nan Lee - co-chair
  • Tzung-Pei Hong - co-chair
  • Kuo-Sheng Cheng - co-chair
  • Chia-Ping Chen - co-chair
  • Chung-Nan Lee - advisor
  • Files
  • etd-0804118-233344.pdf
  • Indicate in-campus at 2 year and off-campus access at 2 year.
    Date of Submission 2018-09-05

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