Immune Task Allocation for Multi-robot System Based on Adjustment Mechanism of Thymic Peptide
YUAN Ming-Xin1,LI Ping-Zheng1,JIANG Ya-Feng1, WANG Sun-An2
1.School of Mechatronics-Engineering and Automotive, Jiangsu University of Science and Technology, Zhangjiagang 215600
2.School of Mechanical Engineering, Xi′an Jiaotong University, Xi′an 710049
To solve the task allocation in multi-robot system, a thymic peptide based immune task allocation algorithm (TPITAA) is proposed. Inspired by the mechanism of idiotypic network hypothesis, an immune allocation model is constructed according to the stimulation and suppression among the antigen and antibodies. The robot, robot behavior and task are taken as B cell, antibody and antigen respectively. To further improve the allocation efficiency, a thymic peptide feedback function based on movement direction of robot is defined according to the immune adjustment mechanism of thymic peptide, which realizes the self-adjustment of antibody stimulation level and antibody concentration. The simulation results show that the proposed algorithm realizes the automatic allocation for tasks, reduces the completion time, improves the operating efficiency and solves the cooperation handling well in multi-robot system.
YUAN Ming-Xin,LI Ping-Zheng,JIANG Ya-Feng等. Immune Task Allocation for Multi-robot System Based on Adjustment Mechanism of Thymic Peptide[J]. , 2014, 27(5): 472-480.
[1] Gerkey B P, Mataric M J. A Framework for Studying Multi-robot Task Allocation // Schultz A C, Parker L E, Schneider F E, eds. Multi-robot Systems: From Swarms to Intelligent Automata. Dordrecht, Netherlands: Kluwer Academic Publishers, 2003: 15-26 [2] Tao X L, Zheng Y B. Multi-agent Task Allocation Method Based on Auction // Luo Q, ed. Advances in Wireless Networks and Information Systems. Berlin, Germany: Springer-Verlag, 2010: 217-225 [3] Florin L. Self-Organization of Roles Based on Multilateral Negotiation for Task Allocation // Proc of the 9th German Conference on Multi-agent System Technologies. Berlin, Germany, 2011: 173-180 [4] Hu X Q, Xu B G. Task Allocation Mechanism Based on Genetic Algorithm in Wireless Sensor Networks // Proc of the International Conference on Applied Informatics and Communication. Xi′an, China, 2011: 46-58 [5] Prakash A, Deshmukh S G. A Multi-criteria Customer Allocation Problem in Supply Chain Environment: An Artificial Immune System with Fuzzy Logic Controller Based Approach. Expert Systems with Applications, 2011, 38(4): 3199-3208 [6] Gao Y Y, Wei W. Multi-robot Autonomous Cooperation Integrated with Immune Based Dynamic Task Allocation // Proc of the 6th International Conference on Intelligent Systems Design and Applications. Ji′nan, China, 2006: 586-591 [7] Gao Y Y, Wei W. Multi-robot Self-Determination Cooperation Based on Immune Network in An Unknown Environment. Journal of Zhejiang University: Engineering Science, 2006, 40(5): 733-737 (in Chinese) (高云园,韦 巍.未知环境中基于免疫网络的多机器人自主协作.浙江大学学报:工学版, 2006, 40(5): 733-737) [8] Wu H, Tian G H, Huang B. Research on the Collaboration Strategy of Multi-robot for Exploring Unknown Environment. Journal of Shandong University: Engineering Science, 2008, 38(4): 27-31,36 (in Chinese) (吴 皓,田国会,黄 彬.未知环境探测的多机器人协作策略研究.山东大学学报:工学版, 2008, 38(4): 27-31,36) [9] Yuan M X, Wang S A, Wu C Y, et al. Hybrid Ant Colony and Immune Network Algorithm Based on Improved APF for Optimal Motion Planning. Robotica, 2010, 28(6): 833-846 [10] Cayzer S, Aickelin U. A Recommender System Based on Idiotypic Artificial Immune Networks. Journal of Mathematical Modelling and Algorithms, 2005, 4(2): 181-198 [11] Jerne N K. Idiotypic Networks and Other Preconceived Ideas. Immunological Reviews, 1984, 79(1): 5-24 [12] Yuan M X, Wang S A, Wu C Y, et al. APF-Guided Adaptive Immune Network Algorithm for Robot Path Planning. Frontiers of Computer Science in China, 2009, 3(2): 247-255 [13] Ishiguro A, Watanabe Y, Kondo T, et al. A Robot with a Decentralized Consensus-Making Mechanism Based on the Immune System // Proc of the 3rd International Symposium on Autonomous Decentralized Systems. Berlin, Germany, 1997: 231-237 [14] Farmer J D, Packard N H, Perelson A S. The Immune System, Adaptation, and Machine Learning. Physica D, 1986, 2(1/2/3): 187-204
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