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標題: 植基於SoPC的三輪全方位行動服務機器人之適應運動控制、最佳組態和全域路徑規劃
SoPC-Based Adaptive Motion Control, Optimal Configurations and Global Path Planning for Three-Wheel Omnidirectional Mobile Service Robot
作者: 黃旭志
Huang, Hsu-Chih
關鍵字: SOPC;系統晶片;Service Robot;Motion Control;Path Planning;服務機器人;運動控制;路徑規劃
出版社: 電機工程學系所
引用: [1] C. Samson, “Control of chained systems application to path following and time-varying point-stabilization of mobile robots,” IEEE Transactions on Automatic Control, vol.40, no.1, pp.64-77, January 1995. [2] I. Kolmanovsky and N. H. McClamroch, “Developments in nonholonomic control problems,” IEEE Control System Magazine, vol.15, no.6, pp.20-36, 1995. [3] A. Astolfi, “Discontinuous control of nonholonomic systems,” System Control Letter, vol.27, no.1, pp.37-45, January 1996. [4] A. M. Bloch and S. Drakunov, “Stabilization and tracking in the non-holonomic integrator via sliding mode,” System Control Letter, vol.29, pp.91-99, 1996. [5] C. Canudas, B. Siciliano and G. Bastin, Theory of robot control, Springer-Verlag, 1996. [6] Z. P. Jiang and H. Nijmeijer, “Tracking control of mobile robots: a case study in backstepping,” Automatica, vol.33, no.7, pp.1393-1399, 1997. [7] Z. P. Jiang and H. Nijmeijer, “A recursive technique for tracking control of nonholonomic systems in chained form,” IEEE Transactions on Automatic Control, vol.44, no.2, pp.265-279, February 1999. [8] T. C. Lee, K. T. Song, C. H. Lee and C. C. Teng, “Tracking control of unicycle-modeled mobile robots using a saturation feedback controller,” IEEE Transactions on Control Systems Technology, vol.9, no.2, pp.305-318, March 2001. [9] T. H. Li, S. J. Chang and Y. X. Chen, “Implementation of human-like driving skills by autonomous fuzzy behavior control on an FPGA-based car-like mobile robot,” IEEE Transactions on Industrial Electronics, vol.50, no.5, pp.867-880, October 2003. [10] T. Fukao, H. Nakagawa and N. Adachi, “Adaptive tracking control of a nonholonomic mobile robot,” IEEE Transactions on Robotics and Automation, vol.16, no.5, pp.609-615, October 2000. [11] T. H. Li and S. J. Chang, “Autonomous fuzzy parking control of a car-like mobile robot,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: System and Humans, vol.33, no.4, pp.451-465, 2003. [12] K. D. Do, Z. P. Jiang and J. Pan, “Simultaneous tracking and stabilization of mobile robots: an adaptive approach,” IEEE Transactions on Automatic Control,” vol.49, no.7, pp.1147-1151, July 2004. [13] K. D. Do, Z. P. Jiang and J. Pan, “A global output-feedback controller for simultaneous tracking and stabilization of unicycle-type mobile robots,” IEEE Transactions on Robotics and Automation, vol. 20, no. 3, pp. 589-594, June 2004. [14] F. G. Pin and S. M. Killough, “A new family of omnidirectional and holonomic wheeled platforms for mobile robots,” IEEE Transactions on Robotics and Automation, vol.10, no.4, pp.480-489, August 1994. [15] C. Y. Chen, “A study on adaptive fuzzy sliding-mode controller for nonholonomic and holonomic wheeled mobile robots,” Ph.D. dissertation, National Cheng-Kung University, Taiwan, 2008. [16] A. Bétourné and G. Campion, “Dynamic modeling and control design of a class of omnidirectional mobile robots,” Proceeding of the 1996 IEEE International Conference on Robotics and Automation, pp.2810-2815, Minneapolis, Minnesota, April 1996. [17] T. Kalmár-Nagy, R. D'Andrea and P. Ganguly, “Near-optimal dynamic trajectory generation and control of an omnidirectional vehicle,” Robotics and Autonomous Systems, vol.46, no.1, pp.47-64, January 2004. [18] R. L. Williams II, B. E. Carter, P. Gallina and G. Rosati, “Dynamic model with slip for wheeled omnidirectional robots,” IEEE Transactions on Robotics and Automation, vol.18, no.3, pp.285-293, June 2002. [19] B. J. Driessen, “Adaptive global tracking for robots with unknown link and actuator dynamics,” International Journal of Adaptive Control and Signal Processing, vol.20, no.3, pp.123-138, 2006. [20] K. Park, H. Chung and J. G. Lee, “Point stabilization of mobile robots via state-space exact feedback linearization,” Robotics and Computer-Integrated Manufacturing, pp.353-363, 2000. [21] J. M. Yang and J. H. Kim, “Sliding mode control for trajectory tracking of nonholonomic wheeled mobile robots,” IEEE Transactions on Robotics and Automation, vol.15, no.3, pp.578-587, 1999. [22] D. K. Chwa, “Sliding mode tracking control of nonholonomic wheeled mobile robots in polar coordinates,” IEEE Transactions on Control Systems Technology, vol.12, no.4, pp.637-644, 2004. [23] H. C. Huang and C. C. Tsai, “Simultaneous tracking and stabilization of an omnidirectional mobile robot in polar coordinates: a unified control approach,” accepted to be published in Robotica, 2008, doi:10.1017/S0263574708004852. [24] C. L. Lin, H. Y. Jan and T. H. Huang, “Self-Organizing PID control design based on DNA computing method,” Proceedings of IEEE International Conference on Control Application, pp.568-573, 2004. [25] J. C. Gallagher, S. Vigraham and G, Kramer, “A family of compact genetic algorithms for intrinsic evolvable hardware,” IEEE Transactions on Evolutionary Computing, vol.8, no.2, pp.111-126, 2004. [26] J. H. Holland, “Adaptation in natural and artificial systems,” University of Michigan Press, Ann Arbor, 1975. [27] A. E. Eiben, J. D. Smith, Introduction to Evolutionary Computing, Springer, 2003. [28] L. M. Adeleman, “Molecular computing of solutions to combinational problems,” Science, vol.266, pp.1021-1024, 1994. [29] Y. Zhu, Y. Ding, W. Li and L. A. Zadeh, “DNA algorithm of image recognition and its application,” IEEE International Conference on Information Reuse and Integration, pp.375-379, 2006. [30] X. Liu and Y. Li, “Efficient DNA algorithms for chromatic number of graph problems,” IEEE International Conference on Automation and Logistics, pp.450-454, 2007. [31] M. H. Garzon and R. J. Deaton, “Biomolecular computing and programming,” IEEE Transactions on Evolutionary Computation, vol.3, no.3, pp.236-250, 1999. [32] M. S. Jelodar, M. Kamal, S. M. Fakhraie, M. N. Ahmadabadi, “SOPC-based parallel genetic algorithm,” IEEE Congress on Evolutionary Computation, pp.2800-2806, 2006. [33] I. Campo, J. Echanobe, G. Bosque and J. M. Tarela, “Efficient hardware/software implementation of an adaptive neuro-fuzzy system,” IEEE Transactions on Fuzzy Systems, vol.16, no.3, pp.761-778, June 2008. [34] Y. S. Kung and M. H. Tsai, “FPGA-based speed control IC for PMSM drive with adaptive fuzzy control,” IEEE Transactions on Power Electronics, vol.22, no.6, pp.2476-2486, November 2007. [35] S. S. Solano, A. J. Cabrera, I. Baturone, F. J. Moreno-Velo and M. Brox, “FPGA implementation of embedded fuzzy controllers for robotic applications,” IEEE Transactions on Industrial Electronics, vol.54, no.4, pp.1937-1945, August 2007. [36] Y. F. Chan, M. Moallem and W. Wang, “Design and implementation of modular FPGA-based PID controllers,” IEEE Transactions on Industrial Electronics, vol.54, no.4, pp.1898-1906, August 2007. [37] S. H. Han, M. H. Lee and R. R. Mohler, “Real-time implementation of a robust adaptive controller for a robotic manipulator based on digital signal processors,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: System and Humans, vol.29, no.2, pp.194-204, March 1999. [38] D. Zhang and H. Li, “A stochastic-based FPGA controller for an induction motor drive with integrated neural network algorithms,” IEEE Transactions on Industrial Electronics, vol.55, no.2, pp.551-561, February 2008. [39] C. F. Juang and C. H. Hsu, “Temperature control by chip-implemented adaptive recurrent fuzzy controller designed by evolutionary algorithm,” IEEE Transactions on Circuits and Systems-I: Regular Paper, vol.52, no.11, pp.2376-2384, November 2005. [40] C. F. Juang and J. S. Chen, “Water bath temperature control by a recurrent fuzzy controller and its FPGA implementation,” IEEE Transactions on Industrial Electronics, vol.53, no.3, pp.941-949, June 2006. [41] B. E. Bishop, “On the use of redundant manipulator techniques for control of platoons of cooperating robotic vehicles,” IEEE Transactions on Systems, Man, and Cybernetics-Part A, vol.33, no.5, pp.608-615, 2003. [42] J. P. Puga and L. E Chiang, “Optimal trajectory planning for a redundant mobile manipulator with non-holonomic constraints performing push-pull tasks,” Robotica, vol.26, no.3, pp.385-394, 2008. [43] O. Chocron, “Evolutionary design of modular robotic arms,” Robotica , vol.26, no.3, pp.323-330, 2008. [44] Y. S. Kung and G. S Shu, “Design and implementation of a control IC for vertical articulated robot arm using SOPC technology,” Proceedings of IEEE International Conference on Mechatronics, pp.532-536, 2005. [45] J. G. Kang and J. M. Lee, “A study on optimal configuration for the mobile manipulator considering the minimal movement,” Proceedings of IEEE International Symposium on Industrial Electronics, vol.2, pp.546-551, 2000. [46] Y. Ding and L. Ren, “DNA genetic algorithm for design of the generalized membership-type Takagi-Sugeno fuzzy control system,” IEEE International Conference on Systems, Man, and Cybernetics, pp.3862-3867, 2000. [47] K. Kiguchi, K. Watanabe and T. Fukuda, “Trajectory planning of mobile robots using DNA computing,” Journal of Advanced Computational Intelligence and Intelligent Informatics, vol.8, no.3, pp.295-301, 2004. [48] A. Hourtash and M. Tarokh, “Manipulator path planning by decomposition: algorithm and analysis,” IEEE Transactions on Robotics and Automation, vol.17, no.6, pp.842-856, December 2001. [49] L. E. Kavraki, P. Svestka, J. C. Latombe and M. H. Overmars, “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE Transactions on Robotics and Automation, vol.12, no.4, pp.566-580, August 1996. [50] L. E. Kavraki, M. N. Kolountzakis, and J. C. Latombe, “Analysis of probabilistic roadmaps for path planning,” IEEE Transactions on Robotics and Automation, vol.14, no.1, pp.166-171, February 1998. [51] E. Rimon and D. Koditschek, “Exact robot navigation using artificial potential functions,” IEEE Transactions on Robotics and Automation, vol.8, no.5, pp.501-518, October 1992. [52] I. K. Jung, K. B. Hong, S. K. Hong and S. C. Hong, “Path Planning of mobile robot using neural network,” Proceedings of the IEEE International Symposium on Industrial Electronics, vol.3, pp.979-983, Slovenia 1999. [53] H. Surmann, J. Huser and J. Wehking, “Path planning for fuzzy controlled autonomous mobile robot,” 5th International Conference on Fuzzy Systems, vol.3, pp.1660-1665, New Orleans, September, 1996. [54] C. Hocaoglu, and A. C. Sanderson, “Planning multiple paths with evolutionary speciation,” IEEE Transactions on Evolutionary Computation, vol.5, no.3, pp.169-191, June 2001. [55] I. A. Taharwa and A. Sheta and M. A. Weshah, “A mobile robot path planning using genetic algorithm in static environment,” Journal of Computer Science, vol.4, no.4, pp.341-344, 2008. [56] S. Yue, D. Henrich, W. L. Xu and S. K. Tso, “Point-to-point trajectory planning of flexible redundant robot manipulators using genetic algorithms,” Robotica, vol.20, no.3, pp.269-280, May 2002. [57] L. Lin, H. Wang and Q. Wu, “Improved genetic algorithms based path planning of mobile robot under dynamic unknown environment,” Proceeding of the IEEE International Conference on Mechatronics and Automation, pp.1728-1732, Luoyang, China, June 2006. [58] M. Chen and A. M. S. Zalzala, “A genetic approach to motion planning of redundant mobile manipulator systems considering safety and configuration,” Journal of Robotic Systems, vol.14, no.7, pp.529-544, 1997. [59] T. W. Manikas, K. Ashenayi and R. L. Wainwright, “Genetic algorithms for autonomous robot navigation,” IEEE Instrumentation & Measurement Magazine, vol.10, no.6, pp. 26-31, December 2007. [60] K. Watanabe, Y. Shiraishi, S. Tzafestas, J. Tang and T. Fukuda, “Feedback control of an omnidirectional autonomous platform for mobile service robots,” Journal of Intelligent and Robotic Systems, vol. 22, pp. 315-330, 1998. [61] C. L. Phillips and H. T. Nagle, Digital control system analysis and design, 3rd edition, Prentice-Hall, Englewood Cliffs, N.J., 1995. [62] H. C. Huang and C. C Tsai, “Adaptive robust control of an omnidirectional mobile platform for autonomous service robots in polar coordinates,” Journal of Intelligent and Robotic Systems, vol.51, no.4, pp.439-460, 2008. [63] M. Serra, T. Slater, J. C. Muzio and D. M. Miller, “The analysis of one-dimensional linear cellular automata and their aliasing properties,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol.9, no.7. pp.767-778, 1990. [64] Y. Y. Chen and K. Y. Young, “An SOM-based algorithm for optimization with dynamic weight updating,” International Journal of Neural Systems, vol.17, no.3, pp.171-181, 2007. [65] O. Brock, and O. Khatib, “Elastic Strips: a framework for motion generation in human environments,” The International Journal of Robotics Research, vol.21, no.21, pp.1031-1052, 2002. [66] S. Quinlan, Real-time modification of collision-free paths, Ph.D. dissertation, Stanford University, CA, USA, 1994. [67] K. S. Hwang and M. J. Ju, “3D collision-free motion based on collision index,” Journal of Intelligent and Robotic Systems, vol.33, pp.45-62, 2002.
本論文旨在發展植基於系統可程式晶片(SoPC)的三輪全方位行動服務機器人之適應運動控制、最佳組態和全域路徑規劃。卡氏座標空間和極座標空間的適應運動控制器經由適應倒逆步法被合成出來,並完成軌跡追踨和穩定。更進一步的,一個平行DNA演算法 (PDNA) 和平行精英基因演算法 (PEGA) 被發展來解決全方位機器人的冗餘問題和全域路徑規劃問題。一個coarse-grain平行化模型不僅被用在PDNA演算法求解全方位行動服務機器人的最佳組態問題並執行滅火任務,而且也用在PEGA求解全域路徑規劃問題。被發展出來的二個適應運動控制器、PDNA演算法、PEGA也都有效的利用硬體/軟體 協同設計和SoPC技術植基於FPGA晶片內。可重覆使用的IP (Intellectual Property) 元件庫也快速的在FPGA內發展出來並整合同一晶片內的嵌入式處理器和嵌入式即時作業系統,驅動行動機器人追踨軌跡和解決行動機器人的最佳化問題。經由模擬和實驗結果,這二個植基於SoPC 的卡氏座標和極座標的適應動態控制器比傳統的全方位行動平台控制器性能更好。更進一步的,植基於SoPC所發展出來的PDNA 演算法和PEGA 也被證明能更有效的解決最佳化的問題。本論文所提出來的技術能夠提供在自主性行動機器領域的研究中,值得參考的方法。

This dissertation presents SoPC-based embedded adaptive motion controllers in both Cartesian and polar coordinates, optimal configuration, and global path planning for three-wheel omnidirectional mobile service robot. The adaptive Cartesian-space and polar-space motion controllers are synthesized via adaptive backstepping to achieve both trajectory tracking and stabilization. Moreover, a parallel deoxyribonucleic acid (PDNA) algorithm and a parallel elite genetic algorithm (PEGA) are presented to solve the redundant inverse kinematic problem and global path planning problem for the omnidirectional mobile robot. A coarse-grain parallel model is not only used to the PDNA algorithm for optimal configurations of an omnidirectional mobile service robot performing fire extinguishment task, but also applied to the PEGA for global path planning. The proposed two adaptive motion controllers, coarse-grain PDNA algorithm and PEGA have been efficiently implemented into field-programmable gate array (FPGA) chips using the hardware/software co-design technique and SoPC (System-on-a-Programmable-Chip) technique. The reusable IP (Intellectual Property) core library has been rapidly developed in FPGA chips by incorporating with the embedded processor and the real-time operating system (RTOS) in the same chip to drive the mobile robot to follow the desired trajectory and solving the optimal problems for omnidirectional mobile robot. Through simulations and experimental results, the proposed SoPC-based adaptive controllers in both Cartesian and polar space outperform conventional controllers for three-wheel omnidirectional mobile platform. Furthermore, the proposed SoPC-based PDNA algorithm and PEGA are shown more powerful to solve the optimal problems. The proposed techniques may provide useful references for professionals working in the field of autonomous mobile robotics.
其他識別: U0005-2101200909380600
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