( 您好!臺灣時間:2021/05/17 08:42
字體大小: 字級放大   字級縮小   預設字形  


研究生(外文):Wei-Wen Liu
指導教授(外文):Guan-Chun Luh
外文關鍵詞:Mobile robotArtificial immune systemObstacle avoidance.
  • 被引用被引用:2
  • 點閱點閱:208
  • 評分評分:
  • 下載下載:52
  • 收藏至我的研究室書目清單書目收藏:0
在本篇論文中,首先,提出了反射式類免疫網路(Reactive Immune Network)應用在智能機器人於未知環境中的巡航策略,除了建立一套詳細的類免疫系統數學模型,還探索研究類免疫系統中的自我組織能力、適應學習能力以及免疫的回饋方法,藉由融合改良過的虛擬目標方法來解決行動機器人陷入局部空間解的問題,這些環境都是由先前學者們設計出來的許多陷阱型態的環境。經由模擬與實作的結果驗證得知,行動機器人都可以有效的追蹤目標並且防止碰撞上障礙物。
在靜態障礙物的研究之後,繼續提出了力場免疫網路(Potential Field Immune Network)應用在行動機器人的追蹤靜止或移動中的目標與閃躲行動障礙物,利用障礙物速度的方法(Velocity Obstacle)計算行動障礙物和行動機器人是否會發生碰撞,力場免疫網路每一時刻會先讓行動機器人閃躲最危險最有可能優先碰撞到的行動障礙物,整個免疫網路的反應還包含了模糊邏輯和基因演算法。根據模擬的結果可以得知此網路的應用可以讓行動機器人有效的適應於動態的環境當中。
Autonomous mobile robots have a wide range of applications in industries, hospitals, offices, and even the military, due to their superior mobility. In order to adapt the robot's behavior to any complex, varying and unknown environment, autonomous robot must be able to maneuver effectively in its environment and to achieve its goals while avoiding collisions with static and moving obstacles.
In this dissertation, first, a Reactive Immune Network (RIN) is proposed and employed to intelligent mobile robot navigation strategies within unknown environments. Rather than building a detailed mathematical model of immune systems, we try to explore the principle in immune network focusing on its self-organization, adaptive learning capability and immune feedback. A modified virtual target method is integrated to solve the local minima problem. Several trap situations designed by early researchers are adopted to evaluate the performance of the proposed immunized architecture. Simulation and experiments results show that the mobile robot is capable of avoiding obstacles, escaping traps, and reaching goal efficiently and effectively.
After static obstacles’ research, continue proposing a potential field immune network (PFIN) for dynamic motion planning of mobile robots in an unknown environment with moving obstacles and fixed/moving goal. The Velocity Obstacle method is utilized to determine the imminent collision obstacle. Subsequently, PFIN is implemented to guide the robot avoid collision with the most danger object at every time instant. The overall response of the immune network is calculated by the aid of fuzzy logic and genetic algorithms. Simulation results are presented to verify the effectiveness of the proposed architecture in dynamic environments with single and multiple fix/moving obstacles.
1.1 Introduction 1
1.2 Literature Review 3
1.2.1 Stationary Environment 4
1.2.2 Dynamic Environment 10
1.3 Contributions of the Dissertation 17
1.4 Organization of the Dissertation 19
2.1 Biological Immune System 21
2.2 Artificial Immune System 25
3.1 Reactive Immune Network 35
3.2 Local Minimum Recovery 42
3.3 Simulation and Experiment 52
3.4 Summary 65
4.1 Velocity Obstacle Method 67
4.2 Potential Field Immune Network 72
4.3 Optimization of the Stimulation and Suppressive Affinity 79
4.4 Simulation Results 81
4.5 Summary 88
5.1 Conclusions 89
5.2 Future Works 91
Baraquand, J.; and Latombe, J.C. (1990). A Monte-Carlo algorithm for path planning with many degrees of freedom, Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1712-1717, Cincinnati, OH, May, 1990.

Borenstein, J. and Koren, Y. (1989). Real-time obstacle avoidance for fast mobile robots. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 19, No. 5, pp.1179–1187.

Carpin, S and Pillonetto, G. (2003). Robot Motion Planning Using Adaptive Random Walks. International Conference on Robotics & Automation, Taipei, Taiwan, September 14-19, 2003, pp. 3809-3814.

Canham, R., Jackson, A.H. and Tyrrell, A. (2003). Robot error detection using an artificial immune system, 2003. Proceedings. NASA/DoD Conference on Evolvable Hardware, (9-11, July 2003), 199 – 207.

Carneiro, J.; Coutinho, A.; Faro, J. and Stewart, J. (1996). A model of the immune network with B-T cell co-operation I-prototypical structures and dynamics, Journal of theoretical Biological, Vol.182, No.4, 1996, pp. 513-529.

Chakravarthy, A. and Ghose, D. (1998). Obstacle Avoidance in a Dynamic Environment: A Collision Cone Approach, IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, Vol.25, No.5, 1998, pp. 562-574.

Chazelle, B. (1987). Approximation and decomposition of shapes. In Advances in robotics. Vol. I. Algorithmic and geometric aspects of robotics (Eds J. T. Schwartz and C. K. Yap), 1987, pp. 145–185 (Lawrence Erlbaum, Hillsdale, New Jersey).

Chang, H. (1996). A new technique to handle local minima for imperfect potential field based motion planning, Proceedings of the IEEE International Conference on Robotics and Automation, pp. 108-112, Minneapolis, Minnesota, April, 1996.

Chatterjee, R. and Matsuno, F. (2001). Use of single side reflex for autonomous navigation of mobile robots in unknown environments, Robotics and Autonomous Systems, Vol.35, No.2, 2001, pp. 77-96.

Conn, R. A. and Kam, M. (1998). Robot motion planning on N-dimensional star worlds among moving obstacles. IEEE Trans. Robotic. Autom., 14(2): 320-325.

Costa Branco, P.J., Dente, J.A. and Vilela Mendes, R. (2003). Using immunology principles for fault detection, IEEE Transactions on Industrial Electronics, 50(2) (2003), 362-373.

Dasgupta, D. (1997). Artificial neural networks and artificial immune systems: similarities and differences, IEEE International Conference on Systems, Man, and Cybernetics, pp. 873-878, Orlando, Florida, October, 1997.

Dasgupta, D. (1999). Artificial Immune Systems and Their Applications, Springer-Verlag, ISBN 3-540-64390-7, Berlin Heidelberg.

de Castro, L.N. and Jonathan, T. (1999). Artificial immune systems: A new Computational Intelligence Approach, Springer-Verlag, ISBN 1-85233-594-7, London.

de Castro, L.N. (2002). Comparing immune and neural networks, Neural Networks, 2002. SBRN 2002. Proceedings. VII Brazilian Symposium on 2002, pp.250-255.

Duan, Q.J.; Wang, R.X.; Feng, H.S. and Wang, L.G. (2004). An immunity algorithm for path planning of the autonomous mobile robot, IEEE 8th International Multitopic Conference, pp. 69-73, Lahore, Pakistan, December, 2004.

Duan, Q.J.; Wang, R.X.; Feng, H.S. and Wang, L.G. (2005). Applying synthesized immune networks hypothesis to mobile robots, IEEE International Conference on Autonomous Decentralized Systems, pp. 69-73, Chengdu, China, April, 2005.

Erdmann, M. and Lozano-Perez, T. (1986). On Multiple Moving Objects, Algorithmica, 1986, 2(4):477—521.

Farmer, J.D.; Packard, N.H. and Perelson, A.S. (1986). The immune system adaptation, and machine learning, Physica, Vol.22-D, Vol.2, No.1-3, 1986, pp. 187-204.

Ferrari, C.; Pagello, E.; Ota, J.; and Arai, T. (1998). Multi-robot motion coordination in space and time, Robotics and Autonomous Systems, Vol.25, No.2, 1998, pp. 219-229.

Fiorini, P. and Shiller, Z. (1998). Motion planning in dynamic environments using velocity obstacles, International Journal of Robotics Research, Vol.17, No.7, 1998, pp. 760-772.

Fujimura, K. and Samet, H. (1989). A hierarchical strategy for path planning among moving obstacles, IEEE Trans on Robot and Automat, Vol.5, No.1, 1989, pp. 61-69.

Fujimori, A., Teramoto, M., Nikiforuk, P. N., Gupta, M. M. (2000). Cooperative collision avoidance between multiple mobile robots, Journal of Robotic Systems, Vol.17, Issue 7, 2000, pp. 347-363.

Fujimori, A., Ogawa, Y., Nikiforuk, P. N. (2002). A modification of cooperative collision avoidance for multiple mobile robots using the avoidance circle. Proceedings of the I MECH E Part I Journal of Systems & Control Engineering, Vol. 216, No. 3, 1 June 2002 , pp. 291-299(9).

Fujimori, A. (2005). Navigation of mobile robots with collision avoidance for moving obstacles, Proc Instn Mech Engrs Part I: J Systems and Control Engineering, Vol.219, No.1, 2005, pp. 99-110.

Ge, S. S. and Cui, Y. J. (1989). Dynamic motion planning for mobile robots using potential field method, Autonomous Robots, Vol.13, No.3, 1989, pp. 207–222.

Guldner, J. and Utkin, V.I. (1995). Sliding mode control for gradient tracking and robot navigation using artificial potential fields, Robotics and Automation, IEEE Transactions on Vol. 11, Issue 2, April 1995 pp. 247-254.

Hart, E.; Ross, P.; Webb, A. and Lawson, A. (2003). A role for immunology in “next generation” robot controllers, Lecture Notes in Computer Science, Vol.2787, 2003, pp. 46-56.

Hightower, R.; Forrest, S. and Perelson, A. S. (1995). The evolution of emergent organization in immune system gene libraries, Proceedings of Sixth International Conference on Genetic Algorithms, pp. 344-350, Pittsburgh, PA, July, 1995.

Hoffmann, G.W. (1989). The immune system: a neglected challenge for network theorists, IEEE International Symposium on Circuits and Systems, pp. 1620-1623, Portland, OR, May, 1989.

Ishida, Y. (1997). The immune system as a prototype of autonomous decentralized systems: an overview, Proceedings of Third International Symposium on autonomous decentralized systems, pp. 85-92, Berlin, Germany, April, 1997.

Ishiguro, A.; Watanabe, Y. and Uchikawa, Y. (1995). An immunological approach to dynamic behavior control for autonomous mobile robots, International Conference on Intelligent Robots and Systems-Vol. 1, 1995, pp. 495-500.

Im, K.Y, Oh, S.Y., and Han, S.J. (2002). Evolving a Modular Neural Network-Based Behavioral Fusion Using Extended VFF and Environment Classification for Mobile Robot Navigation. IEEE Transactions on Evolutionary Computation, Vol. 6, No. 4, 2002, pp. 413-419.

Jerne, N.K. (1973). The immune system, Scientific American, Vol.229, No.1, 1973, pp. 52-60.

Khatib, Oussama (1986). Real-time obstacle avoidance for manipulators and mobile robot, The International Journal of Robotics Research, Vol. 5, No. 1, 1986, pp.90-98.

Kleinstein, S.H. and Seiden, P.E. (2000). Simulating the Immune System, IEEE Computer Simulations, July/August 2000, Vol. 2, No. 4, pp. 69-77.

Kubota, N.; Morioka, T.; Kojima, F. and Fukuda, T. (2001). Learning of mobile robots using perception-based genetic algorithm, Measurement, Vol.29, No.3, 2001, pp. 237-248.

Latombe, J.C. (1991). Robot Motion Planning, Kluwer Academic Publishers, Dordrecht, 1991.

Lee, D.-W. and Sim, K.-B. (1997). Artificial immune network-based cooperative control in collective autonomous mobile robots, IEEE International Workshop on Robot and Human Communication, pp. 58-63, Sendai, Japan, September, 1997.

Lee, D.-J.; Lee, M.-J.; Choi, Y.-K. and Kim, S. (2000). Design of autonomous mobile robot action selector based on a learning artificial immune network structure, Proceedings of the fifth Symposium on Artificial Life and Robotics, pp. 116-119, Oita, Japan, January, 2000.

Lee, S.; Adams, T.M. and Ryoo, B.-Y. (1997). A fuzzy navigation system for mobile construction robots, Automation in Construction, Vol.6, No.2, 1997, pp. 97-107.

Liu, C.; Marcelo Jr., H.A.; Hariharan, K. and Lim, S.Y. (2000). Virtual obstacle concept for local-minimum-recovery in potential-field based navigation, Proceedings of the IEEE International Conference on Robotics and Automation, pp. 983-988, San Francisco, CA, April 2000.

Luh, G.-C. and Cheng, W.-C. (2002). Behavior-based intelligent mobile robot using immunized reinforcement adaptive learning mechanism, Advanced Engineering Informatics, Vol.16, No.2, 2002, pp. 85-98.

Meshref, H. and VanLandingham, H. (2000). Artificial immune systems: application to autonomous agents, Systems, Man, and Cybernetics, 2000 IEEE International Conference on Vol. 1, Issue, 2000, pp.61-66.

Madlhava, K. and Kalra, P.K. (2001). Perception and remembrance of the environment during real time navigation of a mobile robot, Robotics and Autonomous Systems, Vol.37, No.1, 2001, pp. 25-51.

Mucientes, M.; Iglesias, R.; Regueiro, C. V.; Bugarín, A.; Cariñena, P. and Barro, S. (2001). Fuzzy temporal rules for mobile robot guidance in dynamic environments, IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, Vol.21, No.3, 2001, pp. 391-398.

Meng Joo Er and Chang Deng (2004). Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning, Systems, Man, and Cybernetics, Part B, IEEE Transactions on Vol. 34, Issue 3, June 2004, pp. 1478 – 1489.

Neal, M. and Labrosse, F. (2004). Rotation-invariant appearance based maps for robot navigation using an artificial immune network algorithm, IEEE, Piscataway NJ, ETATS-UNIS, 2004, pp.863-870.

Oprea, M.L. (1996). Antibody repertoires and pathogen recognition: the role of germline diversity and somatic hypermutation, PhD Dissertation, Department of Computer Science, The University of New Mexico, Albuquerque, New Mexico.

O’Rourke, J. and Badler, N. (1979). Decomposition of three dimensional objects into spheres. IEEE Trans. Pattern Analysis Mach. Intell., 1979, 1(3), 295–305.

Pratihar D.K., Deb K., and Ghosh A. (1999). A genetic-fuzzy approach for mobile robot navigation among moving obstacles. International Journal of Approximate Reasoning, Vol. 20, No. 2, February 1999, pp. 145-172(28)

Prassler, E.; Scholz, J. and Fiorni, P. (2001). A robotic wheelchair for crowded public environments, IEEE Robotics and Automation Magazine, Vol.7, No.1, 2001, pp. 38-45.

Park, M.G and Lee, M.C. (2003). Artificial potential field based path planning for mobile robots using a virtual obstacle concept, in: Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2003, pp. 735–740.

Qu, Z.; Wang, J. and Plaisted, C.E. (2004). A new analytical solution to mobile robot trajectory generation in the presence of moving obstacles, IEEE Transactions on Robotics, Vol.20, No.6, 2004, pp. 978-993.

Ratering, S. and Gini, M. (1995). Robot Navigation in a Known Environment with Unknown Moving Obstacles. Autonomous Robots, 1(2):149–165, 1995.

Roitt, I.; Brostoff, J. and Male, D.K. (1998). Immunology, Mosby-Harcourt Publishers Ltd, ISBN 0723429189, London.

Roy, P.K., Kozma, R. and Majumder, D.D. (2002). From Neurocomputation to Immunocomputation - A Model and Algorithm for Fluctuation-Induced Instability and Phase Transition in Biological Systems, IEEE Transactions on evolutionary computation, Vol. 6, No. 3, June 2002, pp.292-305.

Singh, S.P.N and Thayer, S.M. (2002). Immunology Directed Methods for Distributed Robotics: A Novel, Immunity-Based Architecture for Robust Control Coordination, Proceedings of the IROS 2002 Conference, October 2002, 2735–2739.

Tsoularis A. and Kambhampati C. (1998). On-line Planning for Collision Avoidance on the Nominal Path. Journal of Intelligent and Robotic Systems, Vol. 21, No. 4, April 1998, pp. 327-371(45).

Tsoularis, A. and Kambhampati, C. (1999). Avoiding Moving Obstacles by Deviation from a Mobile Robot’s Nominal Path. The International Journal of Robotics Research, Vol. 18, No. 5, 454-465, 1999.

Timmis, J.; Neal, M. and Hunt, J. (1999). Data analysis using artificial immune systems, cluster analysis and Kohonen networks: some comparisons, IEEE International Conference on Systems, Man, and Cybernetics, pp. 922-927, Tokyo, Japan, October, 1999.

Vadakkepat, P., Tan, K. C., and Wang, M. L (2000). Evolutionary artificial potential fields and their application inreal time robot path planning. Proceedings of the 2000 Congress on Evolutionary Computation.2000:256-263.

Vargas, P.A.; de Castro, L.N.; Michelan, R. and Von Zuben, F.J. (2003). Implementation of an Immuno-Gentic Network on a Real Khepera II Robot, IEEE Congress on Evolutionary Computation, pp. 420-426, Canberra, Australia, December, 2003.

Xu , W.L., Tso, S.K. and Fung, Y.H. (1998). Fuzzy reactive control of a mobile robot incorporating a real/virtual target switching strategy, Robotics Autonom. Syst. 23 (1998) 171–186.

Xu, W.L. (2000). A virtual target approach for resolving the limit cycle problem in navigation of a fuzzy behaviour-based mobile robot, Robotics and Autonomous Systems, Vol.30, No.4, 2000, pp. 315-324.

Yun, X. and Tan, K.-C. (1997). A wall-following method for escaping local minima in potential field based motion planning, Proceedings of the IEEE International Conference on Advanced Robotics, pp. 421-426, Monterey, CA, July, 1997.
第一頁 上一頁 下一頁 最後一頁 top