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研究生: 羅凱文
Kai-Wen Lo
論文名稱: 設計與實作一適用於自主移動機器人的導航系統
Design and Implementation of a Navigation System for Autonomous Mobile Robot
指導教授: 許健平
Jang-Ping Sheu
口試委員:
學位類別: 碩士
Master
系所名稱: 資訊電機學院 - 資訊工程學系
Department of Computer Science & Information Engineering
畢業學年度: 96
語文別: 英文
論文頁數: 43
中文關鍵詞: 無線感測網路混合架構定位導航系統閃避障礙物
外文關鍵詞: wireless sensor networks, hybrid architecture, localization, navigation system, obstacle avoidance
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  • 機器人導航系統,是成功的機器人的最重要也最基本的元件之一。一個機器人的導航系統是該機器人能傑出地移動的關鍵。在本篇論文中,我們提出一適用於自主移動機器人的導航系統。我們的導航系統是由基於行為與基於模型這兩種導航系統所混合而成。在我們的系統中,由基於行為子系統來負責低階的即時反應行為,由基於模型子系統來負責做高階的有計畫性的行動。此外,我們的系統能跟無線感測網路來通訊,並可利用無線感測網路的定位技術來幫忙校正機器人的估計位置。當機器人將前往一目的地時,我們的系統將利用基於模型子系統來計算出從機器人到該目的地的路徑。然後,它會把此路徑切分成許多虛擬點,而基於行為子系統將會逐一地接近每一個虛擬點。如果有若干障礙物妨礙到,導航系統將使用我們的避障演算法來閃避這些障礙物並確保機器人能持續地前往目的地。因此,我們的機器人將會正確地抵達目的地。而且,我們使用多執行緒技術來建構我們的導航系統。如此一來,我們的系統便可以同時執行重要的模組們且能更加有效率地利用多核心處理器。根據我們的實驗結果,我們的導航系統在有障礙物的走廊之中能有效地導航機器人,並且可以應用廣泛。


    The robotic navigation system is one of the most important and fundamental components of the successful robots. A navigation system of a robot is the key to the excellent motion of the robot. In this thesis, a navigation system for autonomous mobile robot is proposed. Our navigation system is a hybrid of behavior-based and model-based navigation systems. In our system, behavior-based subsystem is in charge of low-level reactive actions, and model-based subsystem is responsible for high-level planned actions. Besides, our system can communicate with wireless sensor network and utilize the localization technology of wireless sensor network to calibrate the estimated position of the robot. When the robot is going to leave for a destination, our system will utilize model-based subsystem to compute a path from the robot to the destination. Then, it divides this path into many virtual points, and the behavior-based subsystem is going to approach each virtual point in turn. If there are some obstacles in the way, the navigation system will use our obstacle avoidance algorithm to avoid these obstacles and keep the robot toward the destination. Therefore, our robot will arrive at the destination correctly. Furthermore, we use multi-thread technology to establish our navigation system. Thus, our system can run important modules concurrently and can utilize the multi-core processor more efficiently. Based on our experimental results, our navigation system can navigate the robot in the passages with obstacles effectively and would be applied extensively.

    Chapter 1 Introduction ............................................................................. 1 Chapter 2 Preliminary .............................................................................. 5 2.1 Behavior Based System ........................................................................................ 7 2.2 Model Based System ............................................................................................ 9 Chapter 3 System Architecture and Algorithms .................................. 14 3.1 Behavior Based Algorithms ............................................................................... 16 3.2 Model Based Algorithms ................................................................................... 21 Chapter 4 System Implementation and Experiments ......................... 30 Chapter 5 Conclusion ............................................................................. 40 References ................................................................................................ 41

    [1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless Sensor Networks: a Survey,” Computer Networks, Vol. 38, No. 4, pp. 393-422, Mar. 2002.
    [2] M. A. Batalin, G. S. Sukhatme, and Myron Hattig, “Mobile Robot Navigation using a Sensor Network,” Proceedings of IEEE International Conference on Robotics and Automation , Vol. 1, pp. 636-641, 2004.
    [3] J. Borenstein, and Y. Koren, “Obstacle Avoidance with Ultrasonic Sensors,” IEEE Journal of Robotics and Automation, Vol. 4, Issue 2, pp. 213-218, Apr. 1988.
    [4] J. Borenstein, and L. Feng, “Measurement and Correction of Systematic Odometry Errors in Mobile Robots,” IEEE Transactions on Robotics and Automation, Vol. 12, Issue 6, pp. 869-880, Dec. 1996.
    [5] R. Braunstingl, P. Sanz, and J.M. Ezkerra, “Fuzzy Logic Wall Following of a Mobile Robot Based on the Concept of General Perception,” Proceedings of the 7th International Conference on Advanced Robotics, pp. 67-376, Sept. 1995.
    [6] R. A. Brooks, “a Robust Layered Control System for a Mobile Robot,” IEEE Journal of Robotics and Automation, Vol. 2, Issue 1, pp. 14 - 23, Mar. 1986.
    [7] R. Dechter, and J. Pearl, “Generalized Best-First Search Strategies and the Optimality of A*,” Journal of the Association for Computing Machinery, Vol. 32, No. 3, pp. 505-536, 1985.
    [8] M. W. M. Gamini Dissanayake, P. Newman, S. Clark, H. F. Durrant-Whyte, and M. Csorba, “A Solution to the Simultaneous Localization and Map Building (SLAM) Problem,” IEEE Transactions on Robotics and Automation, Vol. 17, Issue 3, pp. 229-241, Jun. 2001.
    42
    [9] D. Fox, W. Burgard, and S. Thrun, “Markov Localization for Mobile Robots in Dynamic Environments,” Journal of Artificial Intelligence Research, pp. 391-427, 1999.
    [10] J. Jackson, “Microsoft Robotics Studio: a Technical Introduction,” IEEE Robotics & Automation Magazine, Vol. 14, Issue 4, pp. 82-87, Dec. 2007.
    [11] J. Kramer, and M. Scheutz, “Development Environments for Autonomous Mobile Robots: a Survey,” Autonomous Robots, Vol. 22, No. 2, pp. 101-132, Feb. 2007.
    [12] T. S. Levitt, and D. T. Lawton, “Qualitative Navigation for Mobile Robots,” Artificial Intelligence, Vol. 44, Issue 3, pp. 305-360, Aug. 1990.
    [13] V. J. Lumelsky, and A. A. Stepanov, “Path-Planning Strategies for a Point Mobile Automaton Moving Amidst Unknown Obstacles of Arbitrary Shape,” Algorithmica, Vol. 2, No. 1, pp. 403-430, 1987.
    [14] MobileRobots Inc., Pioneer 3 Operations Manual.
    [15] MobileRobots Inc., Advanced Robotics Interface for Applications (ARIA), http://www.activrobots.com/SOFTWARE/aria.html.
    [16] M. E. Munich, J. Ostrowski, and P. Pirjanian, “ERSP: a Software Platform and Architecture for the Service Robotics Industry,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 460-467, Aug. 2005.
    [17] Y.-K. Na, and A.-Y. Oh, “Hybrid Control for Autonomous Mobile Robot Navigation Using Neural Network Based Behavior Modules and Environment Classification,” Autonomous Robots, Vol. 15, Issue 2, pp. 193-206, Sept. 2003.
    [18] F. Qureshi, D. Terzopoulos, and Ross Gillett, “The Cognitive Controller: a Hybrid, Deliberative/Reactive Control Architecture for Autonomous Robots,” Proceedings of the 7th International Conference on Innovations in Applied
    43
    Artificial Intelligence, pp. 1102-1111, 2004.
    [19] S. I. Roumeliotis, and G. A. Bekey, “Bayesian Estimation and Kalman Filtering: a Unified Framework for Mobile Robot Localization,” Proceedings of IEEE International Conference on Robotics and Automation, Vol. 3, pp. 2985-2992, Apr. 2000.
    [20] J.-P. Sheu, J.-M. Li, and C.-S. Hsu, ”a Distributed Location Estimating Algorithm for Wireless Sensor Networks,” IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, Vol. 1, pp. 218- 225, June 2006.
    [21] J.-P. Sheu, K.-Y. Hsieh, and P.-W. Cheng, "Design and Implementation of Mobile Robot for Nodes Replacement in Wireless Sensor Networks," Journal of Information Science and Engineering, Vol. 24, pp. 393-410, Feb. 2008.
    [22] J. Simpson, C. L. Jacobsen, and M. C. Jadud, “Mobile Robot Control: the Subsumption Architecture and Occam-Pi,” Communicating Process Architectures, 2006.
    [23] M. Terwilliger, A. Gupta, V. Bhuse, Z. H. Kamal, and M. A. Salahuddin,“A Localization System using Wireless Network Sensors: A Comparison of Two Techniques,” Proceedings of the First Workshop on Positioning, Navigation and Communication, Hannover, Germany, Mar. 2004.
    [24] Texas Instruments Inc. CC2431DK Development Kit User Manual Rev. 1.5.
    [25] D. Toal, C. Flanagan, C. Jones, and B. Strunz, “Subsumption Architecture for the Control of Robots,” University of Limerick, 1996.

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