APF-guided adaptive immune network algorithm for robot path planning

Abstract

Inspired by the mechanism of Jerne’s idiotypic network hypothesis, a new adaptive immune network algorithm (AINA) is presented through the stimulation and suppression between the antigen and antibody by taking the environment and robot behavior as antigen and antibody respectively. A guiding weight is defined based on the artificial potential field (APF) method, and the guiding weight is combined with antibody vitality to construct a new antibody selection operator, which improves the searching efficiency. In addition, an updating operator of antibody vitality is provided based on the Baldwin effect, which results in a positive feedback mechanism of search and accelerates the convergence of the immune network. The simulation and experimental results show that the proposed algorithm is characterized by high searching speed, good convergence performance and strong planning ability, which solves the path planning well in complicated environments.

Keywords

artificial potential field / immune network / path planning / Baldwin effect

References

[1] Ge S S, Cui Y J. Dynamic motion planning for mobile robots using potential field method. Automous Robots, 2002, 13(l3): 207-222

[2] Wei C Z, Ma Z, Chang J H. Reduction of visibility graph on global path planning for mobile robot. In: Proceeding of Chinese Control Conference. Piscataway: IEEE, 2006, 1605-1608

[3] Liu J, Yang D Y. Path planning based on double-layer genetic algorithm. In: Proceedings of 3rd international conference on natural computation. Piscataway: IEEE, 2007, 357-361

[4] Saska M, Macas M, Preucil L. Robot path planning using particle swarm optimization of ferguson splines. In: Proceedings of 11th IEEE International Conference on Emerging Technologies and Factory Automation. Piscataway: IEEE, 2006, 833-839

[5] Liu G Q, Li T J, Li Y P. The ant algorithm for solving robot path planning problem. In: Proceedings of 3rd international conference on information technology and applications. Piscataway: IEEE, 2005, 25-27

[6] Ishiguro A, Shirai Y, Kondo T, . Immunoid: an architecture for behavior arbitration based on the immune networks. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway: IEEE, 1996, 1730-1736

[7] Luh G C, Liu W W. Reactive immune network based mobile robot navigation. In: Proceedings of 3rd International Conference on Artificial Immune Systems. Berlin: Springer, 2004, 119-132

[8] Vargas P A, De Castro L N, Michelan R. Implementation of an immuno-genetic network on a real Khepera II robot. In: Proceedings of International Conference on Evolutionary Computation. Piscataway: IEEE, 2003, 420-426

[9] Li J H, Wang S A. Model of immune agent and application in path finding of autonomous robots. In: Proceedings of International Conference onMachine Learning and Cybernetics. Piscataway: IEEE, 2003, 1961-1964

[10] Whitbrook A M, Aickelin U, Garibaldi J M. Idiotypic immune networks in mobile-robot control. In: Proceedings of IEEE Transactions on Systems, Man, and Cybernetics, Part B. Piscataway: IEEE, 2007, 37(6): 1581-1598

[11] Chen Xi, TanG Z, Jiang B. Real-time optimal path planning for mobile robots based on immune genetic algorithm. Journal of Central South University (Science and Technology), 2008, 39(3): 577-583

[12] Wang S A, Zhuang J. An immunity algorithm for path finding and optimizing of the moving robot. Journal of System Simulation, 2002, 14(8): 995-997

[13] Zhuang J, Wang S A. Further study of robot path planning algorithm based on artificial immune net theory. Journal of System Simulation, 2004, 16(5): 1017-1019

[14] Jerne N K. The immune system. Scientific American, 1973, 229 (suppl 1): 52-60

[15] Cayzer S, Aickelin U. A recommender system based on idiotypic artificial immune networks. Journal of Mathematical Modelling and Algorithms, 2005, 4(2): 181-198

[16] Jerne N K. Idiotypic networks and other preconceived ideas. Immunological Rev, 1984, 79: 5-24

[17] Ge S S, Cui Y J. New potential functions for mobile robot path planning. IEEE Transactions on Robotics And Automation, 2000, 16 (5): 615-620

[18] Khatib O. Real-time obstacle avoidance for manipulators and mobil robot. International Journal of Robotics Research, 1986, 5 (suppl 1): 90-98

[19] Sun Y F, Zhang C K. Baldwin effect based chaotic parallel genetic algorithm with variable-scale learning. In: Proceedings of International Conference on Machine Learning and Cybernetics. Piscataway: IEEE, 2007, 1003-1008

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag Berlin Heidelberg