臺灣博碩士論文加值系統

這篇論文提出一個基於人類狀態及其他機器人狀態的機器人導航演算法，而這個導航演算法是為了實現人類與機器人的和諧共存。隨著機器人應用的增加，未來將有越來越多的機器人進入我們的生活空間，然而機器人在多人類及多機器人的環境中的導航術很少被這領域的研究員所考量。當機器人在一個存在人類及不同功用的機器人環境中進行導航時，機器人應不單只具有基本的避障及到達目的地的能力，在其導航過程中還必須注意其行動是否對人類或處於同一環境中的其他機器人構成干擾。為了解決這問題，我們提出了一些和諧規條讓機器人遵守，這些規條讓一個機器人在多人及多機器人環境中能安全及暢順地導航。根據這些規條，我們提出了一個以人為本的敏感性導航術（HCSN）。心理學上提出人類都有其個人空間，這個個人空間的大小是根據人與人的關係及其心理因素所決定的，HCSN考慮人類的個人空間以及其他機器人的工作空間，我們把這些空間模擬成有優先權的敏感場，機器人把這些敏感場作為導航上的考量，達到有社會接受度的行動。

This paper proposes a navigation algorithm that considers the states of humans and other robots in order to achieve harmonious coexistence between robots and humans. Robot navigation in the presence of humans and other robots is rarely to be considered in the field of robotics so far. When navigating through humans and robots with different functions, a robot should not only pay attention to obstacle avoidance and goal seeking, it should also take care of whether it interferes with other people or robots. To deal with this problem, we propose several harmonious rules, which guarantee a safe and smooth navigation in multi-human and multi-robot (MHMR) environment. Based on those rules, a practical navigation method–human-centered sensitive navigation (HCSN)–is proposed. HCSN considers the fact that both humans and robots have sensitive zones depending on their security regions or on psychological feeling of people. We model these zones as various sensitive fields with priorities, whereby robots tend to yield socially acceptable movements.

誌謝 i
中文摘要 iii
ABSTRACT iv
CONTENTS v
LIST OF FIGURES vii
LIST OF TABLES ix
Chapter 1 Introduction 1
1.1 Background and Related Work 2
1.2 System Overview 3
1.3 Contributions 5
1.4 Thesis Organization 6
Chapter 2 Preliminaries 7
2.1 Artificial Potential Field Method 7
2.2 Nearness Diagram Navigation 8
2.3 Finite State Machine 11
2.4 Proxemics 13
Chapter 3 Human-Centered Sensitive Navigation 15
3.1 Harmonious Rules 15
3.1.1 Six Harmonious Rules 15
3.1.2 Safety and Deadlock Analysis 16
3.2 Various Sensitive Fields 18
3.3 Human-Centered Sensitive Navigation 23
3.3.1 Communication Signal between Robots 23
3.3.2 Sensitive Fields Sensing 24
3.3.3 Self-Situation Identification 28
3.3.4 Motion Planner 30
3.3.5 Complexity Analysis 35
Chapter 4 Simulations and Experimental Results 36
4.1 Simulations 36
4.1.1 Simulation 1 36
4.1.2 Simulation 2 37
4.1.3 Simulation 3 39
4.1.4 Simulation 4 42
4.2 Experimental Results 43
4.2.1 Experiment 1 44
4.2.2 Experiment 2 46
Chapter 5 Conclusions and Future Works 49
REFERENCE 51

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