Skip to main content
Springer Nature Link
Log in

An algorithm for cooperative task allocation in scalable, constrained multiple robot systems

  • Original Research Paper
  • Published:
Intelligent Service Robotics Aims and scope Submit manuscript

Abstract

The current trends in the robotics field have led to the development of large-scale multiple robot systems, and they are deployed for complex missions. The robots in the system can communicate and interact with each other for resource sharing and task processing. Many of such systems fail despite the availability of necessary resources. The major reason for this is their poor coordination mechanism. Task planning, which involves task decomposition and task allocation, is paramount in the design of coordination and cooperation strategies of multiple robot systems. Task allocation mechanism allocates the task in a mission to the robots by maximizing the overall expected performance, and thereby reducing the total allocation cost for the team. In this paper, we formulate a heuristic search-based task allocation algorithm for the task processing in heterogeneous multiple robot system, by maximizing the efficiency in terms of both communication and processing cost. We assume a set of decomposed tasks of a mission, which needs to be allocated to the robots. The near-optimal allocation schemes are found using the proposed peer structure algorithm for the given problem, where the number of the tasks is more than the robots present in the system. The cost function is the summation of static overhead cost of robots, assignment cost, and the communication cost between the dependent tasks, if they are assigned to different robots. Experiments are performed to verify the effectiveness of the algorithm by comparing it with the existing methods in terms of computational time and quality of solution. The experimental results show that the proposed algorithm performs the best under different problem scales. This proves that the algorithm can be scaled for larger system and it can work for dynamic multiple robot system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Zlot R, Stentz A (2005) Complex task allocation for multiple robots. In: Proceedings of IEEE International Conference on Robotics and Automation, pp 1515–1522

  2. Atay N, Bayazit B (2006) Mixed-integer linear programming solution to multi-robot task allocation problem. Technical Report, Department of Computer Science and Engineering, Washington University, WUCSE-54

  3. Bertsekas DP (1992) The auction algorithm for assignment and other network flow problems: a tutorial introduction. Comput Optim Appl 1(1):7–66

    Article  MathSciNet  MATH  Google Scholar 

  4. Dias MB, Stentz A (2001) A market-based approach to multi-robot coordination, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, Tech. Rep. CMU-RI-TR-01-26

  5. Han Y, Li D, Chen J, Li J, Yang X, Hu Y, Zhang G (2010) A multirobots task allocation algorithm based on relevance and ability with group collaboration. Int J Intell Eng Syst 3(2):33–41

    Google Scholar 

  6. Dias MB, Zlot R, Kalra N, Stentz A (2006) Market-based multirobot coordination: a survey and analysis. Proc IEEE 94(7):1257–1270

    Article  Google Scholar 

  7. Tang F (2005) Automated synthesis of multi-robot task solution through software reconfiguration. In: Proceedings of IEEE international conference on robotics and automation, Barcelona, Spain, pp 1501–1508

  8. Botelho S, Alami R (1999) M+: a scheme for multi-robot cooperation through negotiated task allocation and achievement. In: Proceedings of IEEE International Conference on Robotics and Automation, Detroit, Michigan, pp 1234–1239

  9. Parker LE (1998) ALLIANCE: an architecture for fault tolerant multi-robot cooperation. IEEE Trans Robot Autom 14(2):220–240

    Article  Google Scholar 

  10. Werger BB, Matari MJ (2001) Broadcast of local eligibility for multi-target observation. In: Parker LE, Bekey G, Barhen J (eds) Distributed autonomous robotic systems. Springer, Berlin, pp 347–356

  11. Tang F, Parker LE (2005) ASyMTRe: automated synthesis of multi-robot task solutions through software reconfiguration. In: Proceedings of IEEE international conference on robotics and automation, Barcelona, Spain, pp 1501–1508

  12. Gerkey BP, Mataric MJ (2002) Sold!: auction methods for multi-robot control. IEEE Trans Robot Autom (Special Issue on Multi-Robot Systems) 18(5):758–768

    Article  Google Scholar 

  13. Atay N, Bayazit B (2006) Mixed-integer linear programming solution to multi-robot task allocation problem, Tech. Rep. WUCSE-2006-54, Washington University, St. Louis, Mo, USA

  14. Darrah MA, Niland WM, Stolarik BM (2005) Multiple UAV dynamic task allocation using mixed integer linear programming in a SEAD mission. In: InfoTech at Aerospace: advancing contemporary aerospace technologies and their integration, American Institute of Aeronautics and Astronautics, pp 2324–2334

  15. Mosteo AR, Montano L (2006) Simulated annealing for multirobot hierarchical task allocation with flexible constraints and objective functions. In: Proceedings of the workshop on network robot systems, toward intelligent robotic systems integrated with environments

  16. Jones EG, Dias MB, Stentz A (2011) Time-extended multirobot coordination for domains with intra-path constraints. Auton Robots 30(1):41–56

    Article  Google Scholar 

  17. Ding Y, He Y, Jiang J (2003) Multi-robot cooperation method based on the ant algorithm: In: Proceedings of the IEEE Swarm Intelligence Symposium (SIS ’03), pp 14–18

  18. Kmiecik W, Wojcikowski M, Koszalka L, Kasprzak A (2001) Task allocation in mesh connected processors with local search meta-heuristic algorithms. In: Intelligent information and database systems, pp 215–224

  19. Brunet L (2008) Consensus-based auctions for decentralized task assignment. Master thesis, Massachusetts Institute of Technology

  20. Thareswari N, Asokan T (2012) An improved algorithm for constrained multirobot task allocation in cooperative robot tasks. Lect Notes Electr Eng 107:455–466

  21. Andersson M, Sandholm T (2000) Contract type sequencing for reallocative negotiation. In: Proceedings of the 20th International Conference on Distributed Computing Systems (ICDCS 2000), IEEE Computer Society, pp 54–160

Download references

Author information

Authors and Affiliations

  1. Robotics Laboratory, Department of Engineering Design, Indian Institute of Technology Madras, Chennai , 600036, India

    Thareswari Nagarajan & Asokan Thondiyath

Authors
  1. Thareswari Nagarajan
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Asokan Thondiyath
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Thareswari Nagarajan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nagarajan, T., Thondiyath, A. An algorithm for cooperative task allocation in scalable, constrained multiple robot systems. Intel Serv Robotics 7, 221–233 (2014). https://doi.org/10.1007/s11370-014-0154-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11370-014-0154-x

Keywords