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博碩士論文 etd-0804118-233344 詳細資訊
Title page for etd-0804118-233344
論文名稱
Title
應用基於粒子群演算法之無人機群組於空汙染源蒐尋
UAV Path planning and collaborative searching for air pollution source using the Particle Swarm Optimization
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
77
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2018-07-30
繳交日期
Date of Submission
2018-09-05
關鍵字
Keywords
粒子群優化、群智慧、路徑規畫、無人機、導航演算法
Swarm intelligence, UAV, Particle Swarm Optimization (PSO), Navigation algorithm, Path planning
統計
Statistics
本論文已被瀏覽 5724 次,被下載 165
The thesis/dissertation has been browsed 5724 times, has been downloaded 165 times.
中文摘要
空氣污染已成為一個重要的生態問題。透過對污染源的搜索, 可以監控的污染來源。使用無人機做環境監測可以解決這個問題,然而挑戰是,如何讓無人機能智慧協同航行去找出在實際的污染源及分佈。本文運用金体美洲鳊鱼群的合作方式提出了一種新的方法論,它結合粒子群優化與金体美洲鳊鱼群的集體智慧。利用高斯羽流模型描述無人機搜索環境中的污染分佈。此外, 我們提出的方法還包括了無人機群導航的路徑規劃和避碰.

對於路徑規劃, 我們模擬3D 環境下的多障礙物體下來尋找最佳路徑。所提方法皆可以成功產生無衝突的最短路徑。對於無人機的群導航, 模擬環境則採用高斯羽流模型的污染分布,包括考慮溫度、風速等因素。利用所提出的方法, 無人機能夠準確地到達污染源。此外, 通過對無人機的感知設備與搜尋對污染值進行繪製, 來獲得未知污染源的分佈。
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.
目次 Table of Contents
Thesis/Dissertation validation letter i
Aknowledgements ii
Abstract (Chinese) iii
Abstract (English) iv
Table of contents v
List of figures vii
List of tables ix
Chapter 1: Introduction 1
1.1 Overview 2
1.2 Contributions 3
Chapter 2: Literature review and background materials 4
2.1 Basic UAV 4
2.1.2 Coordination systems 4
2.2 Swarm intelligence 4
2.2.1 Nature inspired algorithms (NIA) 5
2.2.2 General principles 5
2.2.3 Ant colony optimization algorithms 6
2.2.4 Bee colony optimization 7
2.3 Particle swarm optimization 8
2.4 Swarm robotics 9
2.4.1 Routing robot swarms with collective intelligence learned from golden shiner fish 9
2.5 Gaussian distribution 10
2.6 Gaussian plume model 11
Chapter 3: Proposed methodology 16
3.1 Path planning algorithm 16
3.1.1 Problem scenario 16
3.1.2 Solution concept 18
3.1.3 Evaluation of cost function 22
3.2 UAV navigation algorithm for searching of pollution source 24
3.3 Problem definition 26
3.4 Analysis of time complexity 31
Chapter 4 Simulations 33
4.1 Simple illustration of PSO algorithm using examples 33
4.2 Simulation in 2D environment 38
4.3 Simulation in 3D environment 44
4.4 Simulation of pollution searching by a group of navigating UAVs 47
4.4.1 Using multivariate Gaussian distribution 47
4.4.2 Pollution search using the Gaussian plume model 51
4.4.3 Evaluation of pollution distribution using sensed information by a swarm of UAVs 56
4.4.4 UAV navigation with multiple pollution sources in the environment 57
Chapter 5: Conclusions 61
References 62
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