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Task Allocation Without Communication Based on Incomplete Information Game Theory for Multi-robot Systems

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Abstract

In the task allocation of multi-robot system, the communication is an important condition to ensure global consistency. Unfortunately, with the popularity of WLAN, the congestion and interference among the bands are particularly severe, making traditional task allocation methods which rely on communication can not work. In this paper, the task allocation without communication is considered to be an incomplete information game, where game theory is employed to realize the task allocation and cooperation of soccer robots. Firstly, the joint probability distribution of the robot type is established according to the distance information, which can be employed to solve the incomplete information static game. Then the ball velocity information is added to modify the joint probability distribution and the incomplete information dynamic game can be solved in the same way. The experimental results show that the success rate of task allocation without communication can be improved effectively using the proposed method.

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References

  1. Bachran, T., Bongartz, H.H.J., Tiderko, A.: A framework for multicast and quality based forwarding in manets. In: Communications and computer networks, pp 120–125 (2005)

  2. Balch, T., Arkin, R.C.: Communication in reactive multiagent robotic systems. Auton. Robot. 1(1), 27–52 (1994)

    Article  Google Scholar 

  3. Choset, H., Pignon, P.: Coverage path planning: the boustrophedon cellular decomposition. In: Field and service robotics, pp 203–209. Springer, Berlin (1998)

  4. Dai, W., Yu, Q., Xiao, J., Zheng, Z.: Communication-Less cooperation between soccer robots. In: Robot World Cup, pp 356–367. Springer, Berlin (2016)

  5. De Heus, P., Hoogervorst, N., Van Dijk, E.: Framing prisoners and chickens: valence effects in the prisoner’s dilemma and the chicken game. J. Exp. Soc. Psychol. 46(5), 736–742 (2010)

    Article  Google Scholar 

  6. Eksin, C., Ribeiro, A.: Distributed fictitious play in potential games of incomplete information. In: 2015 IEEE 54Th annual conference on decision and control (CDC), pp 5190–5196. IEEE, Piscataway (2015)

  7. El-Sherif, S.M., Far, B., Eberlein, A.: Machine learning module to improve communication between agents in multi-agent system. In: 2012 11Th international conference on machine learning and applications (ICMLA), vol. 2, pp 295–300. IEEE, Piscataway (2012)

  8. Fujiwara-Greve, T.: Bayesian nash equilibrium. In: Non-cooperative game theory, pp 133–151. Springer, Berlin (2015)

  9. Fukuda, T., Kawauchi, Y., Asama, H.: Analysis and evaluation of cellular robotics (Cebot) as a distributed intelligent system by communication information amount. In: IEEE international workshop on intelligent robots and systems’ 90.’Towards a New Frontier of Applications’, Proceedings. IROS’90, pp 827–834. IEEE, Piscataway (1990)

  10. Gage, A., Murphy, R., Valavanis, K., Long, M.: Affective task allocation for distributed multi-robot teams (2004)

  11. Gunn, T., Anderson, J.: Effective task allocation for evolving multi-robot teams in dangerous environments. In: Proceedings of the 2013 IEEE/WIC/ACM international joint conferences on Web intelligence (WI) and intelligent agent technologies (IAT), vol. 02, pp 231–238. IEEE Computer Society, Washington (2013)

  12. Harsanyi, J.C., Selten, R., et al.: A general theory of equilibrium selection in games. MIT Press Books 1 (1988)

  13. Kalra, N., Martinoli, A.: Comparative study of market-based and threshold-based task allocation. In: Distributed autonomous robotic systems 7, pp 91–101. Springer, Berlin (2006)

  14. Kitano, H., Asada, M., Kuniyoshi, Y., Noda, I., Osawa, E.: Robocup: the robot world cup initiative. In: Proceedings of the first international conference on autonomous agents, pp 340–347. ACM, New York (1997)

  15. Kobayashi, Y., Hosoe, S.: Cooperative enclosing and grasping of an object by decentralized mobile robots using local observation. Int. J. Soc. Robot. 4(1), 19–32 (2012)

    Article  Google Scholar 

  16. Kraus, S., Plotkin, T.: Algorithms of distributed task allocation for cooperative agents. Theor. Comput. Sci. 242(1), 1–27 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  17. Lu, H., Li, X., Zhang, H., Hu, M., Zheng, Z.: Robust and real-time self-localization based on omnidirectional vision for soccer robots. Adv. Robot. 27(10), 799–811 (2013)

    Article  Google Scholar 

  18. Lu, H., Yang, S., Zhang, H., Zheng, Z.: A robust omnidirectional vision sensor for soccer robots. Mechatronics 21(2), 373–389 (2011)

    Article  Google Scholar 

  19. Meng, Y., Nickerson, J.V., Gan, J.: Multi-robot aggregation strategies with limited communication. In: 2006 IEEE/RSJ international conference on intelligent robots and systems, pp 2691–2696. IEEE, Piscataway (2006)

  20. Milgrom, P.R., Weber, R.J.: Distributional strategies for games with incomplete information. Math. Oper. Res. 10(4), 619–632 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  21. Nickerson, J.V.: A concept of communication distance and its application to six situations in mobile environments. IEEE Trans. Mob. Comput. 4(5), 409–419 (2005)

    Article  Google Scholar 

  22. Noroozi, A.: A novel model for multi-agent systems to improve communication efficiency. In: International conference on computer engineering and technology, 2009. ICCET’09, vol. 2, pp 189–192. IEEE, Piscataway (2009)

  23. Oh, G., Kim, Y., Ahn, J., Choi, H.L.: Market-based task assignment for cooperative timing missions in dynamic environments. J. Intell. Robot. Syst. 87(1), 97–123 (2017)

    Article  Google Scholar 

  24. Rekleitis, I., Lee-Shue, V., New, A.P., Choset, H.: Limited communication, multi-robot team based coverage. In: 2004 IEEE international conference on robotics and automation, 2004. Proceedings. ICRA’04, vol. 4, pp 3462–3468. IEEE, Piscataway (2004)

  25. Roy, N., Dudek, G.: Collaborative robot exploration and rendezvous: algorithms, performance bounds and observations. Auton. Robot. 11(2), 117–136 (2001)

    Article  MATH  Google Scholar 

  26. Rybski, P.E., Larson, A., Veeraraghavan, H., Anderson, M., Gini, M.: Performance evaluation of a multi-robot search & retrieval system: experiences with mindart. J. Intell. Robot. Syst. 52(3-4), 363–387 (2008)

    Article  Google Scholar 

  27. Sun, L., Zhang, Y., Qiao, S.: Summary of multi-robot communication technology. China Science and Technology Information 5, 112 (2008)

    Google Scholar 

  28. Tiderko, A., Bachran, T., Hoeller, F., Schulz, D.: Rose—a framework for multicast communication via unreliable networks in multi-robot systems. Robot. Auton. Syst. 56(12), 1017–1026 (2008)

    Article  Google Scholar 

  29. Turnwald, A., Althoff, D., Wollherr, D., Buss, M.: Understanding human avoidance behavior: interaction-aware decision making based on game theory. Int. J. Soc. Robot. 8(2), 331–351 (2016)

    Article  Google Scholar 

  30. Wang, T., Dang, Q., Pan, P.: A predict-fuzzy logic communication approach for multi robotic cooperation and competition. JCM 6(3), 225–231 (2011)

    Article  Google Scholar 

  31. Wang, T., Dang, Q., Pan, P.: A multi-robot system based on a hybrid communication approach. Studies in Media and Communication 1(1), 91–100 (2013)

    Article  Google Scholar 

  32. Xiang, Z., Wang, Q., Wen, H.: The study of multi-robot communication of autonomous soccer robots based on C/S mode. In: 2010 international conference on multimedia technology (ICMT), pp 1–4. IEEE, Piscataway (2010)

  33. Xiao, J., Xiong, D., Yao, W., Yu, Q., Lu, H., Zheng, Z.: Building software system and simulation environment for robocup Msl soccer robots based on ros and gazebo. In: Robot operating system (ROS), pp 597–631. Springer, Berlin (2017)

  34. Xiong, D., Xiao, J., Lu, H., Zeng, Z., Yu, Q., Huang, K., Yi, X., Zheng, Z.: The design of an intelligent soccer-playing robot. Industrial Robot: An International Journal 43(1), 91–102 (2016)

    Article  Google Scholar 

  35. Yanco, H., Stein, L.A.: An adaptive communication protocol for cooperating mobile robots. In: Meyer, JA, Roitblat, HL, Wilson, S (eds.) (1993) from animals to animats 2. proceedings of the second international conference on simulation of adaptive behavior, pp 478–485. The MIT Press, Cambridge (1993)

  36. Yao, W., Dai, W., Xiao, J., Lu, H., Zheng, Z.: A simulation system based on ros and gazebo for robocup middle size league. In: 2015 IEEE international conference on robotics and biomimetics (ROBIO), pp 54–59. IEEE, Piscataway (2015)

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Acknowledgements

Our work is supported by National Science Foundation of China (NO. 61403409 and NO. 61503401), China Postdoctoral Science Foundation (NO. 2014M562648), and graduate school of National University of Defense Technology. All members of the NuBot research group are gratefully acknowledged.

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Authors and Affiliations

  1. College of Mechatronics and Automation, National University of Defense Technology, Changsha, China

    Wei Dai, Huimin Lu, Junhao Xiao & Zhiqiang Zheng

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  1. Wei Dai
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  2. Huimin Lu
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  3. Junhao Xiao
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  4. Zhiqiang Zheng
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Correspondence to Huimin Lu.

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Dai, W., Lu, H., Xiao, J. et al. Task Allocation Without Communication Based on Incomplete Information Game Theory for Multi-robot Systems. J Intell Robot Syst 94, 841–856 (2019). https://doi.org/10.1007/s10846-018-0783-y

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  • DOI: https://doi.org/10.1007/s10846-018-0783-y

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