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研究生：	范越強 Pham, Viet-Cuong
論文名稱：	強健性同步定位製圖與多機器人環境探索之研究 Investigation of Robust SLAM and Multi-Robot Exploration
指導教授：	莊智清 Juang, Jyh-Ching
學位類別：	博士 Doctor
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2013
畢業學年度：	101
語文別：	英文
論文頁數：	121
中文關鍵詞：	同步定位製圖、整合式探測、重新定位、多機器人、H∞濾波器、降階濾波器
外文關鍵詞：	Simultaneous Localization and Mapping (SLAM), Integrated Exploration, Relocalization, Multiple Robots, H∞ filter, Reduced Order Filter
相關次數：	點閱：108 下載：7
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科技日新月異，機器人的應用也愈來愈廣泛；其應用範圍包含工業應用、探測未知或模糊之地形地物、災區搜救、監控與勘查、危險地區偵查、醫療協助、智慧生活屋、農務工作、娛樂用途等。本論文主要探討機器人學中的兩項問題：同步定位製圖(SLAM)問題以及環境探測問題。
同步定位製圖技術於操作自走機器人之同時可估測出機器人姿態與環境特徵，是一項重要且具挑戰性的任務。特別是，同步定位製圖的實現需兼顧強健性與效能。機器人於未知環境中有可能受建模誤差影響使其不易以統計方式表示。為了減少不確定性與干擾的影響，可考慮應用強健濾波器，例如：濾波器等。然而，強健濾波器需較高的運算資源，複雜且不易實現。因此，本論文提出一降階式濾波器來解決強健同步定位製圖問題，其中機器人動態具有不確定性且環境量測受制於有界但未知的干擾。為了達到效能與強健性同時兼顧，本論文應用狀態分割以及相關的路標標示等相關技術。模擬顯示所提出之降階式濾波器，其不僅具有較低之電腦運算複雜性且與滿階式濾波器結果相似。此外，降階式濾波器比擴展式卡爾曼濾波器以及快速同步定位製圖技術，更具有強健性。
雖然移動機器人環境探測問題已被研究多年，但仍存在許多零星的問題需要被克服。如在傳統探索方法中，機器人姿態是假設已知或可由同步定位與製圖建立方法估測。這些同步定位與環境地圖建立方法僅應用機器人所處理之感知資訊，且缺乏一架構以控制機器人動態來更有效率地呈現同步定位製圖與探索任務。路徑控制策略不但會影響地圖建立的品質，甚至是整體效率。因此，整合式探測方法近年來已被提出於同時考慮定位、建立地圖與移動控制。然而，大多數的整合式探測方法僅處理單一機器人之情況。而本論文提出一整合式探測方法可適用於多機器人之情況。特別是可控制這些機器人使得整體搜尋時間最小化，在此同時機器人姿態與地圖建立，仍具有足夠程度的估測精確度。此方法可更進一步擴展到環境中廣佈的機器人，在處理有限通訊時，亦可平衡環境探測性能與定位品質。電腦模擬結果顯示，所提出之方法比現有的方法具有較佳之成效。

As a result of developing advanced technology and automation, robots are becoming more human-like. They are employed in many applications for improving our daily tasks such as exploration of unknown or distant territories, search and rescue, surveillance, reconnaissance, examination of hazardous areas, assistance in hospital, intelligent home, farm work, and entertainment. This dissertation addresses two fundamental problems in robotics: the simultaneous localization and mapping (SLAM) problem and the exploration problem.
SLAM is an important and challenging task for the operation of autonomous mobile robots in which both the pose of the robot and features of the environment need to be estimated at the same time. In particular, it is desirable to achieve robustness and efficiency in the SLAM implementation. Robots in unknown environment are likely to be subject to modeling errors which cannot be easily characterized in terms of statistical properties. To mitigate the effect of uncertainties and disturbances, robust filters such as the filter can be employed. However, robust filters are complex to implement, demanding a significant amount of computational resources. Therefore, we propose a reduced order filter to solve the robust SLAM problem in which robot dynamics are subject to uncertainties and measurements are subject to bounded-but-unknown disturbances. To achieve both efficiency and robustness, techniques such as state partition and relative landmark representation are employed. Simulations reveal that results obtained from the proposed reduced order filter, which has a lower computational complexity, closely approximate those from the full order filter. Moreover, the reduced order filter is more robust than EKFs and FastSLAM.
Although the problems of mobile robot exploration have been studied for a long time, there are still many practical issues to overcome. For example, in classical exploration approaches, robot poses are assumed to be known or estimated by SLAM. Ordinary SLAM approaches only handle the obtained sensor data and lack a mechanism to control the robot motion to perform the SLAM and exploration tasks more efficiently. It is well known that the path control strategy can have a substantial impact on the quality of the resulting map as well as the overall efficiency. As a result, integrated exploration methods have been proposed recently to simultaneously consider localization, mapping, and motion control. However, most integrated exploration methods only deal with the single robot case. Therefore, we propose an integrated exploration approach for multiple robots. In particular, robots are controlled to minimize the overall exploration time and to maintain a sufficient level of accuracy of the robot pose and map estimates. This approach is further enhanced to globally disperse robots in the environment, to balance the exploration performance and localization quality as well as t o deal with limited communication. Simulation results show that the proposed approaches outperform existing ones.

摘要	I
ABSTRACT	III
ACKNOWLEDGEMENTS	V
TABLE OF CONTENTS	VI
LIST OF TABLES	X
LIST OF FIGURES	XII
CHAPTER 1 INTRODUCTION	1
1	Research Background	1
1.1 Why Robots?	1
1.2 The Simultaneous Localization and Mapping Problem	2
1.3 The Multi-Robot Exploration Problem	2
2 Motivations	3
2.1 A Reduced Order   Filter for the SLAM Problem	3
2.2 An Integrated Approach to the Multi-Robot Exploration Problem	4
3 Dissertation Contribution	6
4 Dissertation Organization	8
CHAPTER 2 ROBUST AND EFFICIENT SLAM VIA REDUCED ORDER H∞  FILTERING	9
1 Navigational maps	9
1.1 Feature-based map	9
1.2 Occupancy grid map	10
2 The SLAM problem	11
2.1 EKF-SLAM	13
2.2 FastSLAM	15
2.3 Multi-Robot SLAM Problem	17
3 The Robust SLAM Problem	18
3.1 The Full Order H∞ Filter	19
3.2 State Partition	21
3.3 The Reduced Order H∞ Filter	22
3.4 Computational Complexity	26
3.5 Relative Landmark Representation	27
4 Simulation Results	33
4.1 Performance Metric	34
4.2 Simulated Dataset	35
4.3 Victoria Park Dataset	42
5 Summary	48
CHAPTER 3 AN INTEGRATED APPROACH TO THE MULTI-ROBOT EXPLORATION PROBLEM	49
1 The Multi-Robot Exploration Problem	49
1.1 The Advantages of Multiple Robots	49
1.2 Line-of-Sight Approaches	50
1.3 Frontier and Utility-based Approaches	51
1.4 Market-based Approaches	52
1.5 Potential Field-based Approaches	52
1.6 Role-based Approaches	53
1.7 Semantic Information-based Approaches	53
1.8 Environment Segmentation-based Approaches	54
1.9 Flocking-based Approaches	54
2 The Integrated Multi-Robot Exploration	54
2.1 Integrated Exploration Problem with Multiple Robots	57
2.2 General Framework	58
2.3 Exploration Phase	62
2.4 Relocalization Phase	64
2.5 Coordination with Limited Communication Range	66
3 Simulation Results and Discussions	67
3.1 Performance Metric	68
3.2 Comparison	68
3.3 Cooperation in the Relocalization Phase	74
3.4 Effect of Limited Communication Range	75
4 Summary	76
CHAPTER 4 AN IMPROVED INTEGRATED MULTI-ROBOT EXPLORATION APPROACH WITH GLOBAL ROBOT DISPERSION	78
1 General Framework	79
2 Exploration Phase with Global Robot Dispersion	80
3 Adaptive Threshold Selection	82
4 Limited Communication Range	84
5 Simulation Results and Discussion	88
5.1 Global Exploration Strategy	89
5.2 Adaptive Uncertainty Threshold	93
5.3 Limited Communication Range	95
6 Summary	98
CHAPTER 5 CONCLUSIONS	99
1 Summary	99
2 Future Research	100
REFERENCES	102
APPENDICES	109
Appendix 1: The reduced order H∞ filter	109
Appendix 2: The full order H∞ filter	112
Appendix 3: Linearization	115
Appendix 4: The reduced order H∞ filter based SLAM algorithm	117
Appendix 5: Initialization of new landmarks	119 
                                    

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