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Automatic Control and Computer Sciences

, Volume 52, Issue 5, pp 371–381 | Cite as

Task Allocation Methods for Homogeneous Multi-Robot Systems: Feed Pushing Case Study

  • A. NikitenkoEmail author
  • E. LavendelisEmail author
  • M. Ekmanis
  • R. Rumba
Article
  • 23 Downloads

Abstract

The paper discuses several options to implement multi-robot systems focusing on coordination and action planning of the system. Particular implementation described in details as a case study presents a solution used in multi-robot system for feed pushing in cattle farm. The paper describes implementation of behavior based robot team control and two different path planning methods ensuring that the feed pushing work is done properly. Conclusions outline suggestions for practical implementations regardless of particular application domain.

Keywords:

task allocation homogeneous multi-robot systems trajectory planning feed pushing robot 

Notes

ACKNOWLEDGMENT

Paper presents research results of the project “Multi robot system for applications in agriculture” funded by Competence center of Latvian Electrical Engineering and electronics Industry. Project number: 2DP/2.1.2.1.1/10/APIA/LIAA/005.

REFERENCES

  1. 1.
    Andersone, I. and Nikitenko, A., Reliable multi-robot map merging of inaccurate maps, Advances in Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection: Proceedings of the 12th International Conference (PAAMS 2014), Spain, Salamanca, June 4–6, 2014, Heidelberg: Springer, 2014, pp. 13–24.Google Scholar
  2. 2.
    FIPA, 2002. FIPA Contract Net Interaction Protocol 6 Specification. http://www.fipa.org/specs/fipa00029/ SC00029H.pdf. Accessed October 5, 2017.Google Scholar
  3. 3.
    Wang, X. and Sheng, B., Multi-robot task allocation algorithm based on anxiety model and modified contract network protocol, Proceedings of 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu, 2017, pp. 1606–1612.Google Scholar
  4. 4.
    Liekna, A., Lavendelis, E., and Grabovskis, A., Experimental analysis of contract NET protocol in multi-robot task allocation, Appl. Comput. Sci., 2012, vol. 13, no. 1, pp. 6–14.Google Scholar
  5. 5.
    Dias, B.M., TraderBots: A new paradigm for robust and efficient multirobot coordination in dynamic environments, Doctoral Dissertation, Robotics Institute, Carnegie Mellon University, 2004.Google Scholar
  6. 6.
    Lavendelis, E., Liekna, A., Nikitenko, A., Grabovskis, A., and Grundspenkis, J., Multi-agent robotic system architecture for effective task allocation and management, Proceedings of the 11th WSEAS Int. Conf. on Signal Processing, Robotics and Automation (ISPRA’12), 2012, pp. 167–174.Google Scholar
  7. 7.
    Nikitenko, A., Grundspenkis, J., Liekna, A., Ekmanis, M., Kulikovskis, G., and Andersone, I. Multi-robot system for vacuum cleaning domain, in Advances in Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection, Springer International Publishing, 2014, vol. 8473, pp. 363–366.Google Scholar
  8. 8.
    Beer, S., The viable system model: Its provenance, development, methodology and pathology, J. Oper. Res. Soc., 1984, vol. 35, no. 1, pp. 7–25.CrossRefGoogle Scholar
  9. 9.
    Pudane, M., Lavendelis, E., and Nikitenko, A., ViaBots: A concept for viability for distributed systems, in Technical Report of the NATO Science and Technology Organization, Applied Vehicle Technology Panel, AVT-241 Specialists Meeting on “Technological and Operational Problems Connected with Unmanned Ground Vehicle (UGV) Application for Future Military Operations,” Rzesow, 2015, pp. 10.1–10.12.Google Scholar
  10. 10.
    Lely, Lely Juno—Automatic Feed Pusher. http://www.agrirobotech.co.kr/catalog/juno.pdf. Accessed November 22, 2017.Google Scholar
  11. 11.
    Epic Games, Unreal Engine 4 Documentation. https://docs.unrealengine.com/latest/INT/Engine/AI/ BehaviorTrees/index.html. Accessed January 15, 2018.Google Scholar
  12. 12.
    Nuverian, NodeCanvas—Visual Behaviour Authoring framework to create advanced AI and Logic, Jan 18, 2016. https://forum.unity.com/threads/nodecanvas-behaviour-trees-state-machines-dialogue-trees.227190/. Accessed January 15, 2018.Google Scholar
  13. 13.
    Lima, P.F., Predictive Control for Autonomous Driving, Stockholm: KTH Electrical Engineering, 2016.Google Scholar
  14. 14.
    Colledanchise, M., Behavior trees in robotics, Doctoral Thesis, Stockholm: KTH Computer Science and Communication, 2017.Google Scholar
  15. 15.
    Open Source Robotics Foundation, A ROS Behavior Tree Library, Mar. 3, 2016. http://wiki.ros.org/behavior_tree.Google Scholar
  16. 16.
    Colledanchise, M., Marzinotto, A., Dimarogonas, D.V., and Ögren, P., Using behavior trees in multi-robot systems, 47st International Symposium on Robotics, 2016.Google Scholar
  17. 17.
    Lavendelis, E., A cloud based knowledge structure update and machine learning framework for heterogeneous multi-agent systems, Int. J. Artif. Intell., 2016, vol. 14, no. 2, pp. 157–170.Google Scholar
  18. 18.
    LaValle, S.M., Rapidly-exploring random trees: A new tool for path planning, Technical Report no. 98-11, Oct. 1998.Google Scholar
  19. 19.
    Melchior, N. and Simmons, R., Particle RRT for path planning with uncertainty, 2007 IEEE International Conference on Robotics and Automation, 2007, pp. 1617–1624.Google Scholar
  20. 20.
    Nikitenko, A., Liekna, A., Andersone, I., Ekmanis, M., and Urtans, E., Mobile robot path planning for indoor use, Proceedings of Engineering for Rural Development, Jelgava, 2014, pp. 366–372.Google Scholar
  21. 21.
    Nikitenko, A., Ekmanis, M., and Liekna, A., RRTs postprocessing for uncertain environments, Proceedings of the 2013 International Conference on Systems, Control and Informatics (SCI 2013), Italy, Venice, 28–30 September, 2013, Venice, 2013, pp. 171–179.Google Scholar
  22. 22.
    Brock, O. and Khatib, O., Elastic strips: A framework for motion generation in human environments, Int. J. Rob. Res., 2002, vol. 21, no. 12, pp. 1031–1052.CrossRefGoogle Scholar
  23. 23.
    Mitchell, H.B., Data Fusion: Concepts and Ideas, Springer-Verlag, 2007.zbMATHGoogle Scholar

Copyright information

© Allerton Press, Inc. 2018

Authors and Affiliations

  1. 1.Department of Artificial Intelligence and Systems Engineering, Riga Technical UniversityRigaLatvia

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