臺灣博碩士論文加值系統

為了達到環境上許多特殊的需求，機器人被做成各種型態。為了自主機器人控制，許多的架構理論也被發展出來。 在現今的研究裡，機器人架構可以約略地被分為三種不同的設計哲學下的種類: 審議式(deliberative) 架構，反應式(reactive)架構，混合式架構。 在本篇的論文裡，我們採用了混合式的設計哲學，而且我們嘗試結合以案例式推理作為我們在混合式架構中審議部分的元件以及用模糊行為控制作為我們在混合式架構中行為反應部分的元件。我們考究了幾個代表性的機器人架構並發現了一些現今機器人架構的挑戰及困難的地方。 於是我們利用了案例式推理的優點以及模糊行為控制的優點來提供一個在建造一個機器人系統架構時較好的替代方案並且將之應用在機器人足球上。我們的架構中的主要優點在於具有學習能力、容易建立策略知識庫以及不需太多的推理過程來達到比較好的反應能力。我們利用案例式推理的幾個優點，如透過先前的解決問題的經驗來解決目前的問題、學習能力以及無須完整的環境模型知識便能進行推理的能力作為我們架構中上層高階推理能力的部分再結合模糊邏輯所提供好的機器操控能力和容易透過模糊語句描述來建構策略的能力來達到我們的理想。最後我們利用電腦程式語言來模擬並且利用機器人足球模擬器來做一個實驗。

For achieving many requirements of environments, the robots are built in many different forms. For controlling an autonomous robot, there are also several approaches to construct a robot. In recent research, the robot architectures can roughly be divided into three design philosophies: deliberative robot architectures, reactive robot architectures and hybrid robot architectures. In this thesis, we adopt the design philosophy of hybrid robot architectures. We try to combine deliberative part using case-based reasoning (CBR) and reactive part using fuzzy behavioral control. We survey several representative robot architectures and discover several challenges in recent robot architectures. We employ the advantages of CBR and fuzzy behavioral control to provide a better alternative for building a robot and apply the robot in robot soccer. The main advantages of our architecture are learning capability, easy construction of the strategy base, and quick reaction without too much planning. We use the learning capability, reasoning without complete world model knowledge, and solving problems through previous experiences of CBR as our high level planner to achieve those advantages which we have mentioned above. Besides, we use the features of fuzzy logic to provide well steering control and easy construction of strategies through fuzzy linguistic description. At last, we also build a robot with computer language to simulate and experiment on robot soccer simulator.

Contents
1. Introduction2. Background and Related Work
2.1 Case-Based Reasoning2.1.1 Case Representation
2.1.2 CBR Cycle
2.1.2.1 Case Retrieval
2.1.2.2 Case Reuse2.1.2.3 Case Revise
2.1.2.4 Case Retaining
2.2 Fuzzy Behavioral Control
2.3 Hybrid architectures( Deliberative/Reactive)
2.3.1 PRS-Lite (Procedural Reasoning System)
2.3.2 AuRa (Autonomous Robot Architecture)
2.3.3 Planner-Reactor
2.3.4 Alantis
2.3.5 SSS (Symbolic Subsumption Servo)
2.3.6 Brief Summary
3.Hybrid Architecture with CBR and Fuzzy Behavioral Control
3.1 Motivation
3.2 Framework with CBR and Fuzzy Behavioral Control
3.3 The Fuzzy Behaviors
3.4 The Execution Cycle of Our CBR Part
3.4.1 The Case Retrieval of CBRFuze
3.4.2 The Case Reuse of CBRFuze
3.4.3 The Case Revise of CBRFuze
3.4.4 The Case Retaining of CBRFuze
4. Comparison with Other Systems
5. Building a CBRFuze System in Robot Soccer Player
5.1 The Robot Soccer Simulator Environment
5.2 The Analysis and Design of Our player
5.3 Brief Summay
6. Conclusion and Future Work
Bibliography

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