在任務順序及時間限制下，多機器人動態任務分配及路徑尋找問題是一個最佳化問題。本研究以貪婪演算法及蟻群演算法，做為任務分配的演算法，配合基於衝突式路徑尋找演算法為基礎，提出動態任務分配及路徑尋找的演算法架構。此架構可用於解決多機器人動態任務分配及路徑尋找問題。本研究提出一優先式的目標函數，將會優先完成任務的所得分數，再以最短所需的時間完成優先分配的任務。本研究所提出的演算法之架構，在預設的環境下執行模擬及實驗，並與單純的貪婪演算法作為比較，驗證所提方法的可行性。而結果表明此架構能優化既有的解決方法並且能夠處理動態環境的問題。

Under precedence and temporal constraints, dynamic task allocation and pathfinding for multi-robot system is to optimize an objective function by assigning tasks and plan paths for robots. In this thesis, a framework based on greedy search and ant colony optimization algorithm, a metaheuristic algorithm, is proposed to deal with the task allocation for multi-robot. Pathfinding is optimally solved through conflict-based search algorithm. With the priority-based objective function, the proposed framework first optimizes the utility of the tasks and then, the makespan of the overall system is optimized. Simulations and experiments are conducted and the results are compared with those from greedy-based algorithm to demonstrate the feasibility of the proposed framework.The results illustrate the improvement in the qualities of the solutions and the ability to handle dynamics in the environment.

摘要-----------------------------------i
Abstract-------------------------------ii
Acknowledgements-----------------------iii
List of Figures------------------------vii
List of Tables-------------------------xi
Nomenclature---------------------------x
Chapter 1: Introduction----------------1
1.1 Background and Motivation-------1
1.2 Literature Review---------------2
1.3 Outline-------------------------7
Chapter 2: DTAPF Problem Formulation---9
2.1 Dynamic MRTA/TOC----------------10
2.2 MAPF----------------------------15
2.3 Dynamic Re-­planning-------------16
Chapter 3: DTAPF framework Design------19
3.1 MRTA/TOC------------------------19
3.1.1 Greedy­-based Search-----------20
3.1.2 Ant Colony Optimization-------23
3.2 MAPF----------------------------27
3.3 Dynamic Re­-planning-------------31
Chapter 4: Robot System Architecture and Localization-----35
4.1 Robot System Architecture-------35
4.1.1 Differential Drive Robot------35
4.1.2 Trajectory Tracking-----------38
4.2 Vision Based Localization-------40
4.2.1 Landmark Based Localization---40
4.2.2 Camera Calibration------------42
4.2.3 Extended Kalman Filter--------45
Chapter 5: Simulation and Experimental Results-----47
5.1 Experiment Design---------------47
5.2 Simulation Results--------------49
5.2.1 Proposed Framework Results----49
5.3 Experimental Results------------60
5.3.1 Localization Results----------64
Chapter 6: Conclusions and Future Work-69
6.1 Conclusions---------------------69
6.2 Future Work---------------------70
References-----------------------------71

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