Journal of Communications, Vol 6, No 3 (2011), 225-231, May 2011
doi:10.4304/jcm.6.3.225-231

A Predict-Fuzzy Logic Communication Approach for Multi Robotic Cooperation and Competition

Tingkai Wang, Quan Dang, Peiyuan Pan

Abstract


This paper presents a new intelligent communication strategy for multi robots’ cooperation and competition, which combines the explicit with implicit communications via using the prediction of robotic behavior and a fuzzy communication approach. The multi robotic system employs a host computer and a team of mobile robots that understand the semantics and grammar as well as observe the codes of conduct. Based on the intelligent communication strategy, two robots playing a zero-sum game of hide-and-seek and two cooperative robots competing against a third robot have been explored. The results of simulation show that the new intelligent communication strategy and the algorithms for cooperation and competition used in the multi-robot system work successfully.


Keywords


Multi-robot systems; Communication Fuzzy logic; Cooperation; Competition

References


[1] Tarique Haider1 and Mariam Yusuf, A Fuzzy Approach to Energy Optimized Routing for Wireless Sensor Networks, The International Arab Journal of Information Technology, Vol. 6, No. 2, April 2009.
PMCid:2907182

[2] Yan Meng Jeffrey, V. Nickerson and Jing Gan, Multi-robot Aggregation Strategies with Limited Communication, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems October 9 - 15, 2006, Beijing, China

[3] DOD, "US Army Survival Manual: FM 21-76," US Department of Defense 1992.

[4] Nickerson, Jeffrey V., and Olariu, Stephan, (2005). A Measure for Integration and its Application to Sensor Networks, WITS 2005.

[5] Nickerson, Jeffrey V., "A Concept of Communication Distance and its Application to Six Situations in Mobile Environments", IEEE Transactions on Mobile Computing, Vo. 4, No.5, Sept./Oct. 2005, pp. 409-419.
doi:10.1109/TMC.2005.60

[6] N. Roy and G. Dudek, “Collaborative robot exploration and rendezvous: algorithm, performance bounds and observations,” J. Autonomous Robot., vol. 11, no. 2, pp. 117-136, 2001.
doi:10.1023/A:1011219024159

[7] Jelle R. Kok, Matthijs T.J. Spaanand Nikos Vlassis, Non-communicative multi-robot coordination in dynamic environments, Robotics and Autonomous Systems Volume 50, Issues 2-3, 28 February 2005, Pages 99-114

[8] Xiao-Lin Long; Jing-Ping Jiang; Kui Xiang; Towards Multirobot Communication, proceeding of IEEE International Conference of Robotics and Biomimetics, ROBIO 2004. pp 307 – 312.

[9] Iqbal, J.; Yousaf, M.M.; Awais, M.M.; A scalable approach of message interpretation by demonstrations for multi-robot communication, proceeding of IEEE 13th International Multitopic Conference, INMIC 2009. pp1-6

[10] Kashyap Shah and Yan Meng, Communication-Efficient Dynamic Task Scheduling for Heterogeneous Multi-Robot Systems, Proceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp230-235, USA, June 20-23, 2007

[11] Ge Ran, Huazhong Zhang, Shulan Gong Improving on LEACH Protocol of Wireless Sensor Networks Using Fuzzy Logic, Journal of Information & Computational Science 7: 3 (2010) 767–775

[12] Tingkai Wang, Quan Dang, Peiyuan Pan, A Path Planning Approach in an Unknown Environment, International Journal of Automation and Computing (IJAC), 7(2) pp310-316, 2010.
doi:10.1007/s11633-010-0508-6

[13] Tingkai Wang, Qasim H Mehdi, Norman E Gough, An integrated navigation system for AGVs based on an environment database, INTERNATIONAL JOURNAL OF COMPUTERS AND THEIR APPLICATIONS, Vol.6, No.1, 1999, p14-24.

[14] Owen, G., 1982, Game theory, Academic press, INC, Second Edition.

[15] Wang, T., Mehdi, Q, Gough, N, 1996., Kinematics models of autonomous guided vehicles and their applications, Proc. of ISCA 5th International Conference, Reno Nevada, USA. 207-211


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