臺灣博碩士論文加值系統

在本論文中， 將以模組化的概念設計行動機器人的所有行為。 吾人採用ART-based AHC架構當成行為的樣本(template)， 在設計每一個行為都以此樣本(template)為基礎架構。 此樣本為加強式學習(reinforcement learning)架構， 藉由環境的回授信號(feedback signal)做內部參數的修正， 讓每個行為都能送出適當的輸出。
吾人參考實際人類駕車習慣， 提出三個模組化的行為， 分別是尋標行為(goal-seeking behavior)， 往空曠方向行為(toward-vacancy behavior)， 遠離障礙物行為(obstacle-avoidance behavior)。 而每個行為都能單獨控制機器人並且可以有自我學習與修正的能力。
在設計融合行為中， 亦採用相同的樣本為基礎， 將數個行為合併為功能更強大的行為。 吾人依行為的功能性和差異性作為融合的依據， 提出了兩個融合行為， 分別是避撞行為(obstacle blending behavior)和 安全行動行為(safe blending behavior)等等。 避撞行為則融合了往空曠方向行為和遠離障礙物行為。 安全行動行為融合了避撞行為與尋標行為。
在電腦模擬中驗證了吾人所提出的方法的可行性， 實際的實驗結果則表示此理論適用於行動機器人且有很好的效果。

In this paper, we use the concept of modularization to design a schema of robotic behavior control. The architecture of an ART-based AHC is used as a template. Every behavior is developed based on this template. Eventually the template is a reinforcement learning architecture. It modifies its inner parameters according to environment feedback signal. Three primitive behaviors are fussed into an autonomous behaving robot. These behaviors are goal-seeking behavior, toward-vacancy behavior, and obstacle-avoidance behavior. Each behavior can control the robot individually with the capability of self-learning. We combine several behaviors into a more functional behavior based on a behavior blending architecture which is constructed by the same module as in behavior control. Two fused behaviors innovated from the primitive behavior according to behavioral functions and differences. They are obstacle blending behavior and safe blending behavior. The obstacle blending behavior is composed of toward-vacancy behavior and obstacle-away behavior; the safe blending behavior combines obstacle blending behavior and goal-seeking behavior, together. Simulations prove the practicability of the proposed methods. It also has good performance in the real robot experiments.

中文摘要……………………………………………………………………….i
英文摘要………………………………………………………………………ii
致謝………………………………………………………………………..iii
目錄………………………………………………………………………..iv
圖目錄………………………………………………………………………..vi
表目錄………………………………………………………………………..ix
第一章 導論…………………………………………………………..1
1.1研究目標………………………………………………………...1
1.2文獻回顧………………………………………………………...2
1.3論文大綱……………………………………………………...2
第二章 行動機器人的軟硬體架構…………………………………
2.1 行動機器人的整體架構……………………………………4
2.2 速度控制系統……………………………………………………...6
2.3 轉向控制系統……………………………………………………....7
2.4 超音波感測系統………………………………………………………8
2.4.1 超音波測距模組……………………….…………………………9
2.4.2 超音波控制電路…………………………………………………...10
2.5 電子羅盤….………………………………………………………….12
2.6 車體運動方程式………………………………………………………13
2.7 作業環境…………………………………………………………..13
第三章 問題描述和相關工作………………………………………..14
3.1 Adaptive Heuristic Critic……………………………………………14
3.2 Modified ART Decoder………………………………………………18
3.3 ART-based Reinforcement Learning………………………………21
3.4 實現樣本………………………………………………………………23
第四章 行動機器人的行為控制………………………………………26
4.1尋標行為…………………………………………………………27
4.1.1 模擬程序 (1)…………………………………………..29
4.1.2 模擬程序 (2)…………………………………………………31
4.1.3 尋標行為的實驗結果…………………………………………33
4.2 往最空矌方向行為……………………………………………34
4.2.1 模擬程序………………………………………………………37
4.3 遠離障礙物行為……………………………………………………39
4.3.1 模擬程序…………………………………………………………40
4.4 結語…………………………………………………………………42
第五章 行動機器人的融合行為………………………………………43
5.1避撞行為…………………………………………………………43
5.1.1 模擬程序………………………………………………………46
5.1.2 避撞行為的實作部分…………………………………………49
5.2 安全行動行為…………………………………………………………50
5.2.1 模擬程序…………………………………………………………53
5.2.2 安全行動行為的實作部分………………………………………55
第六章 結論和未來工作………………………………………………57
參考文獻…………………………………………………………………….58

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