Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/7902
標題: 混合式人工智慧為基礎之停車設計
Autonomous Vehicle Parking Using Hybrid Artificial Intelligent Approach
作者: 李偵魁
Lee, Chen-Kui
關鍵字: Optimization
最佳化
Genetic Algorithm
Petri nets
Fuzzy Control
Parallel Parking
基因演算法
派翠網路
模糊控制
平行停車
出版社: 電機工程學系所
引用: [1] L. E. Dubins, “On curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangents”, American Journal of Mathematic, Vol. 79, pp. 497-516, 1957. [2] J. A. Reeds and L. A. Sheep, “Optimal paths for a car that goes both forwards and backwards”, Pacific Journal of Mathematics, Vol. 145, pp. 367-393, 1990 [3] G. Lamfferriere and H. Sussmann, “Motion planning for controllable systems without drift”, Proc. of the IEEE Int. Conf. on Robotics and Automation, Vol. 2, pp1148-1153, April 1991. [4] R. Murray and S. Sastry, “Steering nonholonomic system using sinusoid”, Proc. of the IEEE Int. Conf. on Decision and Control, pp. 2097-2101, Dec 1990. [5] I. E Paromtchik and C. Laugier, “Autonomous parallel parking of a nonholonomic vehicle”, Proc. of the IEEE Int. Conf. on Vehicle Symposium, pp. 13-18, 1996. [6] S. Yasunobu and Y. Murai, “Parking control based on predictive fuzzy control”, Proc. of IEEE Int. Conf. on Fuzzy system, pp. 1338-1341, Jun 1994. [7] R. E. Jenkins, B. P. Yuhas, “A simplified neural network solution through problem decomposition: the case of the truck backer-upper”, IEEE Trans. on Neural Network, Vol. 4, No. 4, pp. 718-720, July1993. [8] K. Nishimori, S. Hirakawa and H. Tokutaka, “Fuzzification of control timing in driving control of model car”, Proc. 2nd IEEE Conf. on Fuzzy Systems, Vol. 1, pp. 297-302, April, 1993. [9] D. Lyon, “Parallel parking with curvature and nonholonomic constraint”, Proc. of IEEE Intelligent Vehicles Symposium, pp. 341-346, July 1992. [10] K. Watanable, J. Tang, M. Nakamura, S. Koga and T. Fukuda, “A fuzzy-Gaussian neural network and its application to mobile robot control”, IEEE Trans. on Control Systems Technology, Vol. 4, No. 2, pp. 193-199, Mar 1996. [11] K. S. Tang, K. F. Man, Z. F. Liu and S. Kwong, “Minimal fuzzy memberships and rules using hierarchical genetic algorithms”, IEEE Trans. on Industrial Electronics, Vol. 45, No. 1, pp. 162-169, Feb 1998. [12] Y. S. Zhou and L. Y. Lai, “Optimal Design for fuzzy controller by genetic algorithm” , IEEE Trans. on Industrial Applications, Vol. 36, No. 1, pp. 93-97, Jan 2000. [13] S. H. Kim and C. Park, “A self-organized fuzzy controller for wheeled mobile robot using an evolutionary algorithm” , IEEE Trans. on Industrial Electronics, Vol. 48, No. 2, pp. 467-474, April 2001. [14] Li, T.-H.S. and S. J. Chang; “Autonomous fuzzy parking control of a car-like mobile robot”, IEEE Trans. on Systems, Man and Cybernetics, Part A, Vol. 33, No. 4, pp. 451-465, July 2003. [15] C. S. Chiu, K.Y. Lian and L. P, “Fuzzy gain scheduling for parallel parking a car-like robot”, IEEE Trans. on Control Systems Technology, Vol. 13, No. 6, pp. 1084-1092, Nov 2005. [16] M. Md. Suruz and G. Wail, “Intelligent parallel parking of a car-like mobile robot using RFID technology”, ROSE 2007 International Workshop on Robotic and Sensors, pp. 1-6, Oct 2007. [17] F. Hwang, J. King, R. Hale and M. Rokonuzzaman, “Discrete event control of mobile robotic systems with potential applications in healthcare”, Proc. of IEEE 22nd Annual EMBS International Conference, pp. 431-435, July 2000. [18] C. G. Looney, “Fuzzy petri nets for rule-based decisionmaking”, IEEE Trans. on System, Man and Cybernetics, Vol. 18, No. 1, pp. 178-183, Feb 1988. [19] T. Murata, “Petri nets: properties, analysis and applications”, IEEE Proceedings, Vol. 77, No. 4, pp. 541-580, Apr 1989. [20] S. I. Ahson, “Petri nets models of fuzzy neural networks”, IEEE Trans. on System, Man and Cybernetics, Vol. 25, No. 6, pp. 926-932, June 1995. [21] J. C. Canales, L. Xiaoou, and Y. Wen, “Fuzzy knowledge learning via adaptive fuzzy petri net with triangular function model”, The Sixth World Congress on Intelligent Control and Automation, 2006, Vol. 1, pp. 4249-4253, 2006.
摘要: 本論文提出一個混合式人工智慧的控制方法來處理車型機器人的停車最佳化設計。其中包含了基因演算法、以及派翠網路(Petri net)、模糊控制。基因演算法主要用於求出最合適的倒車位置,而派翠網路則取代傳統的流程設計,建立一個在原始路徑受干擾或車格被佔的情況下可替代的停車路徑,模糊控制系統則用於導引控制。 本文首先介紹停車系統的完整架構,接著導入智慧型的平行停車方法,其中包含了路徑規劃,決策選擇以及軌跡追蹤控制器,最後以模擬來驗證此方法的可行性並展現其效能。
This thesis devotes to design and implement a hybrid artificial intelligent control scheme for a car-like mobile robot to perform the task of optimal parking. The control scheme consists of several techniques: genetic algorithm, Petri net and fuzzy control. The binary genetic algorithm is used to determine the feasible parking locations. The Petri-net is used to replace the traditional system flow chart and to plan alternative parking routes especially in global space. The parking routine can be performed if the initial assigned route is interfered or when targeted the parking space is occupied. The fuzzy control system is primarily used to derive along an optimal parking route. In this thesis, the overall configuration of the car parking system is introduced. An intelligent parallel parking control scheme is addressed next which includes a trajectory planner, a decisional kernel and a trajectory tracking controller. The optimal solution is then put into several computer simulations to verify the applicability of the proposed control scheme and to manifest the distinguished features of the proposed scheme.
URI: http://hdl.handle.net/11455/7902
其他識別: U0005-0708200820175300
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-0708200820175300
Appears in Collections:電機工程學系所

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