近年來，由於國際間的競爭與臺灣土地、人事成本的高漲，以及人口老化，加上少子化的問題…等，造成傳統產業競爭力逐漸衰退。傳統產業由於需要人進行協助監督生產流程，需要24小時無時無刻的監控，單只靠人無法負荷這樣的重複工作量，而分擔給多人又會提高人事成本，且需要進行能力訓練，較為不經濟。故我們希望以機器人輔助這部分的需求。
隨著科技進步，產業的高度自動化，移動式機器人須具備智慧化的判斷，如閃避障礙物功能，確保機器人於行走過程中自身安全，避免產生碰撞造成系統損傷，例如於辦公室內，會有很多桌椅以及一些電器設備。若當中有動態障礙物，如人行走過程能否有效判斷及即時反應，且勿干擾辦公人員的工作，並同時做到安全通過複雜障礙環境，是首要探討的課題。
另一重要的功能為路徑規劃，適用於定義行進目標，例如巡邏的路徑引導、全域路徑搜索，順利讓機器人到達指定目標。
遠端監控部分讓機器人的實際狀況及時送給使用者知道，例如說當下巡邏資訊紀錄，是否有異狀，以及讓使用者可以指定機器人平台進行特定行為動作，能夠進行機器人、環境、操作者三方面的互動。
因此本論文提出一個室內整合型兼具閃避障礙物、路徑規劃、遠端監控之未知環境，適用於一般室內辦公室、走廊、工廠自動化監控上的智慧型移動式機器人。

In recent years, due to the international competition, soaring cost of land and personnel, aging population, low birth rate…etc, resulting in the recession of the competitiveness of traditional industries in Taiwan. Manpower is needed to monitor the manufacturing process, however, only a worker can’t endure such kind of repetitive workload; on the other hand, it’s not economic to hire more workers to share the workload. Therefore, we expect robots to replace human resources in the manufacturing process.
With the advance of science and technology, the mobile robot must equip intelligent judgments. For instance, obstacle avoidance, a way to avoid damage being caused by collision with the obstacles. In general, there are some tables, chairs and the electrical equipment in the office. In the dynamic obstacles case, the robot is effective and immediate response to determine while the people are walking, the staff members to maintain a work efficiency, and security through complex environments. It is the primary topics of discussion.
Another important function is path planning, such as the patrol, and the global path planning. Let the mobile robot reach the specified target successfully.
In the remote monitoring case, let users know the actual situation of the mobile robot. For example, records of patrol information and specify the action type to move.
Therefore, this thesis presents a project of the indoor integrated intelligent mobile robots, including obstacle avoidance, path planning, and remote monitoring of the unknown environment.

論文審定書 i
致謝 ii
摘要 iii
Abstract iv
圖次 vii
表次 xi
第一章 緒論 1
1.1 研究動機與目的 1
1.2 文獻回顧 1
1.3 論文貢獻 10
1.4 論文架構 10
第二章 系統架構 11
2.1 超音波感測器 11
2.2 馬達位置控制器暨差速輪系統 12
2.3 馬達驅動器 15
2.4 單晶片微控器 16
2.5 無線射頻模組 20
2.6 輪型機器人平台 21
2.7 筆記型電腦 22
第三章 系統設計 24
3.1 方位推估 24
3.2 閃避障礙物 25
3.3 目標跟隨 27
3.4 旋轉模式 28
3.5 路徑規劃 29
3.6 遠端監控 33
3.7 學習機制 33
第四章 系統實現 40
4.1 模擬系統建立 40
4.2 模糊控制器設計 43
4.3 輪型機器人平台程式規劃設計 46
4.4 周邊電路設計 48
4.5 實作介面程式建立 50
4.6 實作場景建立 54
第五章 模擬及實驗結果 57
5.1 純Fuzzy控制器 58
5.1.1 環境無障礙物-面對終點場景 58
5.1.2 環境無障礙物-背對終點場景 59
5.1.3 環境含障礙物-多障礙物場景 61
5.1.4 環境含障礙物-ㄇ字型場景 62
5.1.5 迷宮場景-無障礙物 64
5.1.6 迷宮場景-含障礙物 66
5.2 Fuzzy-PSO控制器 68
5.2.1 多障礙物場景（一） 68
5.2.2 多障礙物場景（二） 69
5.2.3 多障礙物場景（三） 70
5.2.4 迷宮場景-含障礙物 71
5.3 Fuzzy與Fuzzy-PSO比較 72
第六章 結論與未來展望 73
參考文獻 74

[1] 王維漢，工研院機械所，智慧型機器人技術專輯，機械工業雜誌，第305期，2008/08。
[2] 智慧型機器人及其應用服務，重大產業政策，經濟部工業局全球資訊網：http://www.moeaidb.gov.tw/external/ctlr?PRO=policy.PolicyView&id=1686
[3] BAO Qing-yong , LI Shun-ming , SHANG Wei-yan, and AN Mu-jin. “A Fuzzy Behavior-Based Architecture for Mobile Robot Navigation in Unknown Environments”, International Conference on Artificial Intelligence and Computational Intelligence, 2009, pp.257-261.
[4] Maulin M.Joshi, and Mukesh A.Zaveri. “Neuro-Fuzzy Based Autonomous Mobile Robot Navigation System”, 2010 11th Int. Conf. Control, Automation, Robotics and Vision, Singapore, 7-10th December , 2010, pp.384-389.
[5] Sungbok Kim, and Hyunbin Kim. “Optimally Overlapped Ultrasonic Sensor Ring Design for Minimal Positional Uncertainty in Obstacle Detection”, International Journal of Control, Automation, and Systems, 2010, pp. 1280-1287.
[6] Rong-Jong Wai, Chia-Ming Liu, and You-Wei Lin, “Design of Switching Path-Planning Control for Obstacle Avoidance of Mobile Robot”, Journal of the Franklin Institute 348, 2011, pp. 718-737.
[7] T. T. Quyen Bui, and Keum-Shik Hong. “Sonar-Based Obstacle Avoidance Using Region Partition Scheme”, Journal of Mechanical Science and Technology 24, 2010, pp.365-372.
[8] Meng Joo Er, and Chang Deng. “Obstacle Avoidance of a Mobile Robot Using Hybrid Learning Approach”, IEEE Transactions on Industrial Electronics, vol. 52, No. 3, June, 2005, pp.898-905.
[9] Chia-Feng Juang, and Chia-Hung Hsu. “Reinforcement Ant Optimized Fuzzy Controller for Mobile-Robot Wall-Following Control”, IEEE Transactions on Industrial Electronics, vol. 56, no. 10, October, 2009, pp.3931-3940.
[10] Hong Qu, Simon X. Yang, Allan R. Willms, and Zhang Yi. “Real-Time Robot Path Planning Based on a Modified Pulse-Coupled Neural Network Model”, IEEE Transactions on Neural Networks, vol. 20, no. 11, November, 2009, pp.1724-1739.
[11] Chia-Feng Juang, and Yu-Cheng Chang. “Evolutionary-Group-Based Particle-Swarm-Optimized Fuzzy Controller With Application to Mobile-Robot Navigation in Unknown Environments”, IEEE Transactions on Fuzzy Systems, vol. 19, no. 2, April, 2011, pp.379-392.
[12] Daqi Zhu ,Yuanyuan Yang , and Mingzhong Yan, “Path Planning Algorithm for AUV Based on a Fuzzy-PSO in Dynamic Environments”, Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2011, pp.525-530.
[13] Woojin Chung, Seokgyu Kim, Minki Choi, Jaesik Choi, Hoyeon Kim, Chang-bae Moon, and Jae-Bok Song , “Safe Navigation of a Mobile Robot Considering Visibility of Environment”, IEEE Transactions on Industrial Electronics, vol. 56, no. 10, OCTOBER 2009, pp.3941-3950.
[14] Baifan Chen, Zixing Cai, Zheng Xiao, Jinxia Yu, and Limei Liu, “Real-time Detection of Dynamic Obstacle Using Laser Radar”, The 9th International Conference for Young Computer Scientists, 2008, pp.1728-1732.
[15] Junfei Qiao, Zhanjun Hou, and Xiaogang Ruan, “Application of Reinforcement Learning Based on Neural Network to Dynamic Obstacle Avoidance”, Proceedings of the 2008 IEEE International Conference on Information and Automation, Zhangjiajie, China, June 20 -23, 2008, pp.784-788
[16] Tzuu-Hseng S. Li, Ying-Chieh Yeh, Jyun-Da Wu, Ming-Ying Hsiao, and Chih-Yang Chen, “Multifunctional Intelligent Autonomous Parking Controllers for Carlike Mobile Robots”, IEEE Transactions on Industrial Electronics, vol. 57, no. 5, May 2010, pp.1687-1700.
[17] Kyoungmin Lee and Wan Kyun Chung , “Effective Maximum Likelihood Grid Map With Conflict Evaluation Filter Using Sonar Sensors”, IEEE Transactions on Robotics, vol. 25, no. 4, August 2009, pp.887-901
[18] Antonio Franchi, Luigi Freda, Giuseppe Oriolo, and Marilena Vendittelli. “The Sensor-based Random Graph Method for Cooperative Robot Exploration”, IEEE/ASME Transactions on Mechatronics, vol. 14, NO. 2, APRIL 2009, pp.163-175.
[19] 陳柏昌、周榮華，以超音波感測器於機器人環境地圖之建立，博士論文，國立成功大學工程科學系，11月26日，2007年。
[20] Benjamín Tovar, Rafael Murrieta-Cid, and Steven M. LaValle. “Distance-Optimal Navigation in an Unknown Environment Without Sensing Distances”, IEEE Transactions on Robotics, vol. 23, no. 3, June 2007, pp.506-518.
[21] Myungsik Kim, and Nak Young Chong. “Direction Sensing RFID Reader for Mobile Robot Navigation”, IEEE Transactions on Automation Science and Engineering, vol. 6, no. 1, January 2009, pp.44-54.
[22] Sunhong Park and Shuji Hashimoto, “Autonomous Mobile Robot Navigation Using Passive RFID in Indoor Environment”, IEEE Transactions on Industrial Electronics, Vol.56, No.7, July 2009, pp.2366-2373.
[23] Ting-L Chien, Hsin-Chou Lai, Yung-Chien Lin, and Yung-Chin Lin. “Dynamic Programming Algorithm Based Path Planning of The Multiple Robot System”, Second International Conference on Digital Manufacturing & Automation, 2011, pp.469-474.