本論文主要在探討四輪轉向與四輪驅動移動式機器人之自我定位與控制應用。結合了四輪轉向與四輪驅動移動方式於機器人並整合許多方面研究題材，包含了機器人運動控制、基於已知地圖自我定位、避障導航、路徑規劃以及行為策略。四輪轉向與四輪驅動使機器人可以更多元且靈活的方式移動。本論文藉由瞬時迴轉中心（ICR）方法來實現機器人的運動控制。以Rao-Blackwellised 粒子濾波器融合里程計資訊與雷射量測距離資訊來達到機器人的自我定位。機器人導航的部分使用A*演算法實現路徑規劃與動態視窗(DWA)避障方法執行安全的防範。最後實驗結果展現所設計之機器人系統，可成功完成2011新光智慧型保全機器人競賽的各項任務。

This thesis is mainly to concern the localization and control applications of a four-wheel steering and four-wheel drive (4WS4WD) mobile robot. The 4WS4WD combines with the benefits of the 4WD structure and the advantages of a 4WS system, which has the better performance of lateral dynamics. There are many topics combined with the 4WS4WD mobile robot such as motion control, self-localization based on a known map, obstacle avoidance, path planning and control strategy. The Instantaneous Center of Rotation (ICR) has been adopted for the 4WS4WD mobile robot. The localization based on known map carries out the Rao-Blackwellised particle filter method and it is computed via the distance measurement by a laser range finder and the movement of the robot estimated by an odometer. A mobile robot navigation system is used by the A* algorithm for path planning and the Dynamic Window Approach (DWA) safely controlled by the mobile robot is used for obstacle avoidance. The experimental results demonstrate that the robot can successfully conquer many kinds of terrain and carry out all the tasks in the 2011 SKS Intelligent Security Robot Competition.

[1] R. Leenen, J. J. Ploeg, H. H. Nijmeijer, L. Moreau, and F. Veldpaus, Motion control design for a 4ws and 4wd overactuated vehicle, Master Thesis, Department Mechanical Engineering Dynamics and Control Technology Group, Eindhoven University of Technology, Eindhoven, January 2004
[2] F. Yili, H. Xu, W. Shuguo, and M. Yulin, “A navigation robot with reconfigurable chassis and bionic wheel,” in Proc. 2004 IEEE International Conference Robotics and Biomimetics, ROBIO 2004, 2004 pp. 485-489.
[3] 2011 SKS Intelligent Security Robot Competition.
http://www.robotown.org.tw/attachment.php?s=f61a8a88ac416a275e2670fe9263a783&attachmentid=2961&d=1305098657
[4] SolidWorks. Available: http://www.solidworks.com/
[5] SICK. Available: http://www.sick.com/group/EN/home/Pages/Homepage1.aspx
[6] ROOTIS. Available: http://www.robotis.com/zbxe/intro
[7] S. Thrun, W. Burgard, and D. Fox, “Robot perception,” in Probabilistic Robotics. Cambridge, MA: MIT Press, 2005, pp. 171–172.
[8] J. Laaksonen, Mobile Robot Localization Using Sonar Ranging and WLAN Intensity Maps, Master Thesis, Department of Information Technology, Lappeenranta University of Technology, Lappeenranta, February 2007.
[9] X. He, H. Wanyi, P. Fangliang, X. Kai, Y. Shuanghe, G. Xiaozhi, O. Qiuyun, C. Qing, and L. Zhimao, “Maneuver control of mobile robot based on equivalent instantaneous center of rotation in rough terrain,” in Proc. 2007 International Conference Mechatronics and Automation, ICMA 2007, 2007 pp. 405-410.
[10] X. He, X. Kai, W. Ping, B. S. Marie, W. Ran, and J. Bao, “Maneuver control and kinematical energy analysis of a robot based on instantaneous center of rotation,” in Proc. 2006 1st IEEE International Conference E-Learning in Industrial Electronics, 2006, pp. 101-106.
[11] K. C. Cheng, Design and Implementation of SLAM and Behavior Strategy for Home Service Robot, Master Thesis, Department of Electrical Engineering, National Cheng Kung University, Taiwan, June 2010.
[12] R. E. Kalman, “A new approach to linear filtering and prediction problems 1,” Transactions of the ASME–Journal of Basic Engineering, vol. 82, no. Series D, pp. 35–45, 1960.
[13] K. Kanazawa, D. Koller, and S. Russell, “Stochastic simulation algorithms for dynamic probabilistic networks,” in Proc. the Eleventh Annual Conference on Uncertainty AI., 1995, pp. 346-351.
[14] J. Maccormick, “A probabilistic exclusion principle for tracking multiple objects,” International Journal of Computer Vision, vol. 39, no. 1, pp. 57–71, 2000.
[15] N. Gordon, D. Salmond, and A. Smith, “Novel approach to nonlinear non-Gaussian Bayesian state estimation,” IEE Proceedings F: Radar and Signal Processing, pp. 107–113, 1993.
[16] X. Xu and B. Li, “Rao-Blackwellised particle filter with adaptive system noise and its evaluation for tracking in surveillance,” IEEE Transactions on Image Processing, vol. 16, no. 3, pp. 838–849, 2007.
[17] C. Stachniss, G. Grisetti and W. Burgard, “Recovering particle diversity in a Rao-Blackwellized particle filter for SLAM after actively closing loops,” in Proc. of the IEEE International Conference on Robotics and Automation (ICRA), pp. 667–672, 2005.
[18] S. B. Wibowo and M. Klepal, “Rao-Blackwellized particle filter for pattern matching indoor localisation,” in Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS), 2010, 2010, pp. 1-6.
[19] A. Doucet, N. de Freitas, and N. Gordan, Eds., Sequential Monte-Carlo Methods in Practice. New York: Springer-Verlag, 2001.
[20] S. Thrun, D. Fox, W. Burgardz, and F. Dellaert, “Robust Monte Carlo Localization for Mobile Robots,” Artificial Intelligence, vol. 128, pp. 99-141, 2001.
[21] G. Grisettiyz, C. Stachniss, and W. Burgard, “Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling,” in Proc. 2005 IEEE International Conference on Robotics and Automation, ICRA 2005, 2005, pp. 2432-2437.
[22] G. Grisetti, C. Stachniss, and W. Burgard, “Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters,” IEEE Transactions on Robotics, vol. 23, pp. 34-46, 2007.
[23] N. Nilsson, Problem-solving methods in artificial intelligence: McGraw-Hill Pub. Co., 1971.
[24] D. Fox, W. Burgard, and S. Thrun, “Controlling synchro-drive robots with the dynamic window approach to collision avoidance,” in Proc. 1996 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 96, 1996, vol.1283, pp. 1280-1287.
[25] D. Fox, W. Burgard, and S. Thrun, “The dynamic window approach to collision avoidance,” IEEE Transactions on Robotics & Automation Magazine, vol. 4, pp. 23-33, 1997.