傳統檢測水質的工作往往需求大量的人力資源、時間以及成本，此論文的目標在於開發出一整合系統來進行檢測的工作，進而省下這些成本。結合微型光譜儀之移動機器人系統可進行樣品的遠端感測及感知 (存在及濃度)，地面機器人前往目標所在位置，且依循著光譜的特徵來辨識待檢測的液體。此研究利用類神經網路來進行樣本的辨識，藉由實驗後的結果可知，此方法可明確的辨識多混合物的液體。
此研究利用基因演算法來進行路徑規劃，並且導航地面機器人到達目的地。基因演算法具有強大的優化能力，但由於傳統的突變及交配點是經由隨機抽樣的方式所選擇，交配及突變的演化方式可能造成當機器人追蹤優化後的路徑時，與障礙物發生碰撞，另外，需花費較多的時間來搜尋最佳解。在此論文中提中兩種利用演化前所取資訊為基底來進行演化的基因演算法，分別為斯巴達以及自動調適基因演算法。
此論文設計一控制器來進行地面機器人的控制，並且經由里亞普諾夫穩定法則來分析穩定系統之需求，進而達到執行軌跡追蹤之目的，其中，進行路徑規劃之影像是經由裝載在無人機上的攝影機所取得。

Conventional inspection works of water quality require lot of human resource; it is labor intensive, costly and time summing. To overcome those limitations, an integrated system is proposed in this thesis. It is toward the application of quality assurance for drinking water reserve and suspicious materials inspection for law enforcement. A mobile robot system with an on-board micro-spectrometer is proposed for sensing and perception of remote specimen (existence and concentration). The Unmanned Ground Vehicle (UGV) is navigated to the target location to sample the specimen, followed by the pattern recognition of the spectral characteristics. Artificial Neural Network (ANN) is implemented for pattern recognition. The statistical data shows the proposed approach can specifically recognize the multi-components mixture solution.
Genetic Algorithm (GA) based path planning is developed to navigate the UGV toward the goal in this thesis. Although GA path planning has strong capability of optimization, since the evolutionary point is determined randomly, the conventional crossover and mutation operation might cause the optimal path to be infeasible or request more time for exploring the optimal solution. Two new knowledge based approaches are presented, which are Sparta Genetic Algorithm (SGA) and Auto Fitting Genetic Algorithm (AFGA).
A motion controller is designed and analyzed by Lyapunov stability theory, then implemented to maneuver the robot with guidance of a developed map and the localization determined from a camera mount on Unmanned Aerial Vehicle (UAV).

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