移動機器人被廣泛用於許多產業，甚至如軍事工程、太空探勘、醫療照護及生活服務等應用。然而移動機器人的應用中仍會存在諸如任務分配、路徑搜索與運動規劃等問題。因此，本研究分為三個階段，研發解決上述問題之方案。第一階段開發一種合作式避障的運動規劃演算法，可引導所有機器人前往各自的任務區域開始執行任務。第二階段進行機器人的任務規劃，提出預聚類貪婪啟發式的混合方法，先不考慮避障功能，預規劃一條近似最佳解的閉合路徑以執行所有任務點。最後，於第三階段時，若任兩個任務點之間出現障礙物，每台機器人可應用本研究所提之多目標規劃基因演算法，以避開環境障礙物。本研究提出的系統化方法講究實際執行時的運算效率，兼用中心化的主控電腦及部署於各台機器人上的嵌入式系統，使得整體多機器人系統能在有限的續航時間內儘可能完成大量執行目標。由所有階段的模擬及實驗結果可顯示本研究成果可有效解決複數台移動機器人執行眾多任務時的避障及運動規劃問題。

Mobile robots have been widely employed to many industries, even for the applications such as military engineering, space exploration, medical care, life service, etc. However, among the researches in the multi-robot systems, there still exist several fundamental topics, including the task allocation, route searching and motion planning. Therefore, this work proposes a three-stage solution to concentrate on the abovementioned topics. Firstly, all robots are navigated to their corresponding regions for task execution, by employing the proposed motion planning that combines the algorithms of the informed search, the cooperative evading strategy and the deadlock intervener. Next, the task allocation is developed for each robot by hybridizing the pre-clustering and greedy heuristic. At the final stage, if there exist obstacles between any two successive tasks, each robot can search the collision-free route to execute the required tasks using the multi-objective evolutionary algorithm based on the genetic algorithm. Considering that robots have to execute all demanded tasks under limited time and computation power, the computing efficiency of this work is proved by binding the schemes of a centralized computer and an embedded system on each robot. Both of the simulation and experimental results show that this work can efficiently solve the motion planning, task allocation and route searching for multiple mobile robots within tractable time.

論文摘要 ......................................................I
Abstract .......................................................II
Acknowledgements ..............................................III
Table of Contents ..............................................IV
List of Figures ..............................................VIII
List of Tables ...............................................XIII
Nomenclature ..................................................XIV
Chapter 1 Introduction ..........................................1
1.1 Background and Motivation ................................1
1.2 Literature Review ...................................3
1.2.1 Task Allocation for Multiple Robots ....................3
1.2.2 Route Searching for a Single Robot .....................8
1.2.3 Motion Coordination for Multiple Agents ...............11
1.3 Contribution ............................................17
1.4 Organization of this Dissertation .......................18
Chapter 2 Problem Overview .....................................19
2.1 Terminology .............................................19
2.1.1 Agents and Robots .....................................19
2.1.2 Actuators and Sensors .................................20
2.1.3 Composite Environment .................................20
2.1.4 Constraints ...........................................21
2.1.5 Task Allocation .......................................21
2.1.6 Route Searching .......................................22
2.1.7 Motion Planning .......................................23
2.1.8 Centralized and Decentralized Schemes .................24
2.2 Problem Definition ......................................24
2.2.1 Kinematic Modeling for the Mobile Robot ...............26
2.2.2 Task Allocation for Multiple Robots ...................28
2.2.3 Single-Robot Route Searching ..........................29
2.2.4 Multi-Agent Motion Coordination .......................29
Chapter 3 Task Allocation for Multiple Robots ..................31
3.1 Formulation .............................................31
3.2 Pre-Clustering and Construction Method ..................33
3.2.1 Pre-Clustering ........................................34
3.2.2 Construction Method ...................................36
3.3 Variants of Greedy Heuristics ...........................38
3.3.1 Entire Greedy Heuristic (EG) ..........................39
3.3.2 Binary Greedy Heuristic (BG) ..........................40
3.3.3 Recursive Greedy Heuristic (RG) .......................42
3.4 Results and Discussion ..................................44
3.4.1 Environment Setup .....................................44
3.4.2 Demonstration..........................................44
3.4.3 Evaluation and Discussion..............................48
Chapter 4 Single-Robot Route Searching .........................54
4.1 Formulation .............................................54
4.1.1 Environment Description ...............................54
4.1.2 Mathematical Modeling .................................55
4.2 Methodology .............................................59
4.2.1 Representation of the Solution ........................62
4.2.2 Parent Selection ......................................63
4.2.3 Crossover Operator ....................................64
4.2.4 Mutation Operator .....................................65
4.2.5 Survivor Selection ....................................66
4.3 Parameter Determination .................................67
4.4 Realization .............................................72
4.4.1 System Architecture ...................................72
4.4.2 Obstacle Localization by the ArUco System .............76
4.4.3 Path Tracker ..........................................79
4.5 Results and Discussion ..................................82
4.5.1 Experimental Results ..................................82
4.5.2 Discussion ............................................87
Chapter 5 Multi-Agent Motion Coordination ......................89
5.1 Formulation .............................................89
5.1.1 Global Configuration ..................................89
5.1.2 Constraints and Mathematical Modeling .................90
5.1.3 Evaluation Metrics ....................................93
5.2 Methodology .............................................93
5.2.1 Global Path Planner with the Reformable-A*.............96
5.2.2 Extended ORCA for the Locomotion Planning .............99
5.2.3 Intervention in Deadlocks ............................107
5.2.4 Complexity Analysis ..................................113
5.3 Results and Discussion .................................115
5.3.1 Maps and Scenarios ...................................116
5.3.2 Parameter Determination ..............................118
5.3.3 Behavioral Demonstration .............................120
5.3.4 Performance Evaluation ...............................123
5.3.5 Experimental Results .................................127
5.3.6 Discussion ...........................................134
Chapter 6 Conclusions and Future Works ........................137
6.1 Conclusions ............................................137
6.2 Future Works ...........................................139
References ....................................................140

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