本論文主要研究模糊推論與元啟發演算法整合後的技術，應用機器人系統在不同工作空間的任務排程策略優化。首先，為了證明改進的元啟發演算法能夠有效提升原始演算法的性能，將收斂因子和非線性系統以及混沌理論的元素整合到社會蜘蛛群演算法和政治演算法。通過各種問題測試的結果證實，這種整合方法能夠提高原始算法的性能，相對於其他演算法，表現更優越。
其次，本文構建一套基於視覺整合的選擇順應性裝配機械手臂，並針對模糊推論系統研究於排程優化決策方法，應用於晶圓檢測排程規劃系統。此系統主要包括兩個階段。第一階段，設計一台具有視覺自動調整辨識特徵點功能的選擇順應性裝配機械手臂，用於檢測晶圓的移載設備。第二階段，開發了一套模糊推論系統結合專家經驗，優化排程程序以完成晶圓檢測，並驗證以最短時間完成所有晶圓檢測項目。
最後，本文提出一套可解決機器人在室內環境中，通過選擇優先整理物品的位置順序問題。研究過程中，藉由生成不同複雜度的環境數據集，將環境資訊轉換成特徵矩陣，並利用元啟發演算法優化模糊推論系統中的規則庫，改善原有的決策系統，再由特徵矩陣和優化的規則庫嵌入到模糊推論系統，利用旅行商問題確定機器人移動的最佳位置順序。通過模擬與實際場域的測試及實驗，驗證所提方法的有效性，顯著提升機器人的整理能力和效率，並在實際應用中產生重要影響。

This dissertation primarily investigates the integration of fuzzy inference and metaheuristic algorithms and explores their applications in optimizing scheduling strategies for robot systems in various workspace tasks. Firstly, to demonstrate the enhanced performance of the metaheuristic algorithm, elements such as convergence factors, nonlinear systems, and chaos theory are integrated into the Social Spider Optimization Algorithm and Political Optimization Algorithm. The results of simulation experiments confirm that this integration approach can improve the performance of the original algorithm, making it superior to other algorithms.
Secondly, this dissertation constructs a vision-based compliant Selective Compliance Assembly Robot arm (SCARA) and conducts research on fuzzy inference systems for scheduling optimization decisions, applied to wafer inspection scheduling planning systems. This system consists of two main stages. In the first stage, a compliant SCARA with visual automatic adjustment of recognition feature points capability is designed for wafer handling. In the second stage, a fuzzy inference system combined with expert knowledge is developed to optimize the scheduling process for wafer inspection, aiming to complete all wafer inspection tasks in the shortest possible time.
Finally, the challenge of robot-assisted tidying in workspaces is addressed by proposing a decision system for selecting priority sequences of tidying up objects. By generating datasets of varying complexities and incorporating environmental information into feature matrices, the improved metaheuristic algorithm is utilized to optimize the rule base of the fuzzy inference system. The feature matrices and optimized rule base are integrated into the fuzzy inference system, which employs the Traveling Salesman Problem (TSP) to determine the optimal sequence of object arrangement. Through simulation and real-world testing, this dissertation validates the effectiveness of the proposed method, significantly enhancing the robot’s tidying capabilities and efficiency, while making a substantial impact in practical applications.

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