Responsibility Area Based Task Allocation Method for Homogeneous Multi Robot Systems

  • Egons LavendelisEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8629)


The paper presents task decomposition and allocation method for multi-robot systems for area coverage tasks. The method is based on the notion of responsibility area which is the part of the environment that is considered to be atomic task which is allocated to a single robot. The responsibility areas are defined based on the equality of the needed amount of work for their processing. The amount of work is calculated based on the particular area and obstacles in it. The task allocation is done in the way that the most suitable responsibility areas are sequentially added to each robot. The main criterion for the task allocation is the distance from the responsibility area to the particular robot. Still the indexing mechanism is introduced to make the robots to process the environment region by region without leaving unprocessed responsibility areas. The method is implemented and tested in the multi-robot system for vacuum cleaning of large areas that cannot be cleaned by a single vacuum cleaning robot.


Task allocation Multi-robot systems Area coverage tasks Responsibility area 


  1. Andersone, I.: The influence of the map merging order on the resulting global map in multi-robot mapping. Sci. J. RTU. 5. Ser. Appl. Comput. Syst. 13, 22–28 (2012)Google Scholar
  2. Andersone, I., Liekna, A., Nikitenko, A.: Mapping implementation for multi-robot system with glyph localisation. Sci. J. RTU. 5. Ser. Appl. Comput. Syst. 14, 67–72 (2013)Google Scholar
  3. Ashlock, D., et al.: A note on general adaptation in populations of painting robots. In: The 2003 Congress on Evolutionary Computation, CEC 2003, vol. 1, pp. 46–53, 8–12 Dec 2003Google Scholar
  4. Choset, H.: Coverage for robotics - a survey of recent results. Annals Math. Artif. Intell. 31, 113–126 (2001)CrossRefGoogle Scholar
  5. Dias, M.B.: TraderBots: a new paradigm for robust and efficient multirobot coordination in dynamic environments. Technical report. Doctoral dissertation, Robotics Institute, Carnegie Mellon University, January 2004Google Scholar
  6. Doroftei, D., De Cubber, G., Chintamani, K.: Towards collaborative human and robotic rescue workers. In: 5th International Workshop on Human-Friendly Robotics (HFR2012), 18–19 October 2012Google Scholar
  7. FIPA 2002, FIPA Contract Net Interaction Protocol Specification. Foundation for Intelligent Physical Agents. (2002). Accessed 21 March 2014
  8. Gerkey, B.P.: On Multi-robot task allocation. Ph.D. Dissertation. University of Southern California Computer Science Department, Aug 2003Google Scholar
  9. Gerkey, B.P., Mataric, M.J.: A framework for studying multi-robot task allocation. In: Schultz, A.C., et al. (eds.) Multi-robot systems: from swarms to intelligent automata, vol. II, pp. 15–26. Kluwer Academic Publishers, The Netherlands (2003)Google Scholar
  10. IRobot Roomba specification. (2014). Accessed 21 March 2014
  11. Lavendelis E., et al.: Multi-agent robotic system architecture for effective task allocation and management. In: Recent Researches in Communications, Electronics, Signal Processing and Automatic: Proceedings of the 11th WSEAS International Conference on Signal Processing, Robotics and Automation (ISPRA ‘12), pp 167–174, United Kingdom, Cambridge, 22–24 February 2012Google Scholar
  12. Liekna, A., Lavendelis, E., Grabovskis, A.: Experimental analysis of contract net protocol in multi-robot task allocation. Sci. J. RTU. 5. Ser. Appl. Comput. Syst. 213, 6–14 (2012)Google Scholar
  13. Liekna, A., Lavendelis, E., Ņikitenko, A.: Challenges in development of real time multi-robot system using behaviour based agents. In: Omatu, S., Neves, J., Corchado Rodriguez, J.M. (eds.) Distributed Computing and Artificial Intelligence. Advances in Intelligent Systems and Computing, pp. 587–598. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. Mancini A., et al.: Coalition formation for unmanned quadrotors. In: Proceedings of the 7th International ASME/IEEE Conference on Mechatronics and Embedded Systems and Applications, pp. 315–320, September 2011Google Scholar
  15. Ņikitenko, A., et al.: Single robot localisation approach for indoor robotic systems through integration of odometry and artificial landmarks. Appl. Comput. Syst. 14(2013), 50–58 (2013)Google Scholar
  16. Palacín, J., et al.: Building a mobile robot for a floor-cleaning operation in domestic environments. IEEE Trans. Instrum. Measur. 53(5), 1418–1424 (2004)CrossRefGoogle Scholar
  17. Satish Kumar, K.N., Sudeep, C.S.: Robots for precision agriculture. In: Electronic Proceedings of 13th National Conference on Mechanisms and Machines (NaCoMM07), Bangalore, India, 12–13 December 2007Google Scholar
  18. Wooldridge, M.: An Introduction to Multi Agent Systems, 2nd edn, p. 484. Wiley, Chichester (2009)Google Scholar
  19. Yamaguchi, Y., et al.: Development of an intelligent robot for an agricultural production ecosystem (viii) – improvement of predator-prey model and analysis of the activity of snail in paddy by image processing. J. Fac. Agric. Kyushu Univ. 55(1), 101–105 (2010)Google Scholar
  20. Young, J.E., et al.: Toward acceptable domestic robots: applying insights from social psychology. Int. J. Soc. Robot. 1(1), 95–108 (2009). Springer, The NetherlandsCrossRefGoogle Scholar
  21. Zlot, R.M., Stentz, A.: Complex task allocation for multiple robots. In: Proceedings of the International Conference on Robotics and Automation, pp. 1515–1522, April 2005Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of System Theory and DesignRiga Technical UniversityLatviaRiga

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