Path Planning and Ripeness Detection for a Lychee Garden Bas
发布时间:2021-08-23 14:29
This dissertation concerns path planning and ripeness detection for an autonomous robot operating in a two-dimensional environment equipped for an imaginary map.This thesis provides the robot and computer employed in it with a relatively simple method for determining paths in a Lychee garden and detecting ripe Lychee from images.This study builds up a picture guided versatile automated robotic path planning for the Lychee garden.The focal point of the research is on rapid image processing,simple...
【文章来源】:华南理工大学广东省 211工程院校 985工程院校 教育部直属院校
【文章页数】:83 页
【学位级别】:硕士
【文章目录】:
Abstract
Abbreviation List
Chapter 1 Introduction
1.1 Introduction
1.2 Motivation
1.3 Research Objectives
1.4 Problem Definition
1.5 Related Works
1.6 Contribution of the Thesis
1.7 Outlines of the Thesis
1.8 Chapter Summary
Chapter 2 Theoretical Background of Path Planning and Detection
2.1 Brief overview of robot Path Planning
2.1.1 Path Planning in Static known environment
2.1.2 Path Planning in Dynamic unknown environment
2.1.3 Popular Path Planning Approaches
2.2 Ripe Lychee fruit detection using Image Processing technique
2.2.1 Purpose of using Image Processing technique
2.2.2 Determination of Ripeness
2.3 Feature-extraction techniques for fruit detection
2.3.1 Color-feature extraction
2.3.2 Shape-feature extraction
2.3.3 Texture-feature extraction
2.4 Path Planning using Image Processing
2.5 Robot and environment simulator
2.6 Chapter Summary
Chapter 3 Color-feature based ripe Lychee fruits detection
3.1 Analysis of Color-feature extraction method
3.2 Chromatic difference Analysis
3.3 Color-thresholding Approach
3.4 Algorithm for automatic detection of ripening Lychee
3.5 Proposed Image Processing techniques
3.5.1 Image acquisition and processing
3.5.2 HSV Color transformation
3.5.3 Contouring around ripe Lychee fruits
3.5.4 Removing image background
3.5.5 Segmentation using Color Thresholding Algorithm
3.6 Review on ripe fruit detection
3.7 Performance of proposed technique
3.8 Impacts on variable image resolution
3.9 Chapter Summary
Chapter 4 Graph-based Lychee garden Path Planning
4.1 Graph-based Path Planning
4.2 Application of Graph theory
4.3 Graph Theory in Image Processing
4.4 Graph Search theory
4.4.1 Informed Path Planning
4.4.2 Uninformed Path Planning
4.4.3 Finding the shortest path in the graph
4.4.4 Related works on the Graph search Algorithm
4.5 Grid-based environment map Modelling
4.6 Robotic navigation
4.7 Data Structures for the map
4.8 Using BFS Algorithm for Lychee garden Path Planning
4.8.1 Algorithm
4.8.2 Pseudo code of BFS Algorithm (G, s)
4.8.3 Search Process
4.9 Overall system procedure
4.10 Comparison of BFS with Dijakstra Algorithm
4.11 Chapter Summary
Chapter 5 Computational Implementation and Result
5.1 Analysis Platform
5.2 Results obtained using the Breadth-First Search algorithm
5.3 Results obtained using different start and end point
5.4 Results obtained using Image processing technique
5.5 Fruit Identifying accuracy rate
5.6 False detection as ripe Lychee fruit
5.7 Overview of the System
Chapter 6 Discussion and Future Works
6.1 Conclusion
6.2 Research Limitation
6.3 Future Works
ACKNOWLEDGEMENT
REFERENCES
攻读硕士学位期间取得的研究成果
附件
【参考文献】:
期刊论文
[1]夜间自然环境下荔枝采摘机器人识别技术[J]. 熊俊涛,林睿,刘振,何志良,杨振刚,卜榕彬. 农业机械学报. 2017(11)
[2]基于彩色信息的树上柑橘识别研究[J]. 徐惠荣,叶尊忠,应义斌. 农业工程学报. 2005(05)
本文编号:3358043
【文章来源】:华南理工大学广东省 211工程院校 985工程院校 教育部直属院校
【文章页数】:83 页
【学位级别】:硕士
【文章目录】:
Abstract
Abbreviation List
Chapter 1 Introduction
1.1 Introduction
1.2 Motivation
1.3 Research Objectives
1.4 Problem Definition
1.5 Related Works
1.6 Contribution of the Thesis
1.7 Outlines of the Thesis
1.8 Chapter Summary
Chapter 2 Theoretical Background of Path Planning and Detection
2.1 Brief overview of robot Path Planning
2.1.1 Path Planning in Static known environment
2.1.2 Path Planning in Dynamic unknown environment
2.1.3 Popular Path Planning Approaches
2.2 Ripe Lychee fruit detection using Image Processing technique
2.2.1 Purpose of using Image Processing technique
2.2.2 Determination of Ripeness
2.3 Feature-extraction techniques for fruit detection
2.3.1 Color-feature extraction
2.3.2 Shape-feature extraction
2.3.3 Texture-feature extraction
2.4 Path Planning using Image Processing
2.5 Robot and environment simulator
2.6 Chapter Summary
Chapter 3 Color-feature based ripe Lychee fruits detection
3.1 Analysis of Color-feature extraction method
3.2 Chromatic difference Analysis
3.3 Color-thresholding Approach
3.4 Algorithm for automatic detection of ripening Lychee
3.5 Proposed Image Processing techniques
3.5.1 Image acquisition and processing
3.5.2 HSV Color transformation
3.5.3 Contouring around ripe Lychee fruits
3.5.4 Removing image background
3.5.5 Segmentation using Color Thresholding Algorithm
3.6 Review on ripe fruit detection
3.7 Performance of proposed technique
3.8 Impacts on variable image resolution
3.9 Chapter Summary
Chapter 4 Graph-based Lychee garden Path Planning
4.1 Graph-based Path Planning
4.2 Application of Graph theory
4.3 Graph Theory in Image Processing
4.4 Graph Search theory
4.4.1 Informed Path Planning
4.4.2 Uninformed Path Planning
4.4.3 Finding the shortest path in the graph
4.4.4 Related works on the Graph search Algorithm
4.5 Grid-based environment map Modelling
4.6 Robotic navigation
4.7 Data Structures for the map
4.8 Using BFS Algorithm for Lychee garden Path Planning
4.8.1 Algorithm
4.8.2 Pseudo code of BFS Algorithm (G, s)
4.8.3 Search Process
4.9 Overall system procedure
4.10 Comparison of BFS with Dijakstra Algorithm
4.11 Chapter Summary
Chapter 5 Computational Implementation and Result
5.1 Analysis Platform
5.2 Results obtained using the Breadth-First Search algorithm
5.3 Results obtained using different start and end point
5.4 Results obtained using Image processing technique
5.5 Fruit Identifying accuracy rate
5.6 False detection as ripe Lychee fruit
5.7 Overview of the System
Chapter 6 Discussion and Future Works
6.1 Conclusion
6.2 Research Limitation
6.3 Future Works
ACKNOWLEDGEMENT
REFERENCES
攻读硕士学位期间取得的研究成果
附件
【参考文献】:
期刊论文
[1]夜间自然环境下荔枝采摘机器人识别技术[J]. 熊俊涛,林睿,刘振,何志良,杨振刚,卜榕彬. 农业机械学报. 2017(11)
[2]基于彩色信息的树上柑橘识别研究[J]. 徐惠荣,叶尊忠,应义斌. 农业工程学报. 2005(05)
本文编号:3358043
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