Skip to main content
SpringerLink
Log in

Market Approaches to the Multi-Robot Task Allocation Problem: a Survey

  • Survey Paper
  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

Market-based methods have received significant attention for solving the multi-robot task allocation problem. They have been used in a variety of multi-robot scenarios, such as patrolling, exploration, pick-and-delivery, and many more. In consequence, the literature on market-based methods is thriving, with many innovative concepts and complex scenarios studied. However, there has been no survey of this literature in the recent years. In this paper, we apply a rigorous systematic literature review method, designed to produce transparent and reproducible meta-analyses, in order to address the need for a survey of the literature of market-based methods applied to the multi-robot task allocation problem. We provide researchers with an introduction to market-based methods, a comprehensive classification of market-based methods, addressing both market and communication schemes, an analysis of the comparative studies on market-based methods, and a discussion of research trends.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Code Availability

The data that describe the selection of the primary studies to be included in this study are available from Github, https://github.com/felixquinton/SMS_MBA_MRTA.

References

  1. Guzzoni, D., Cheyer, A., Julia, L., Konolige, K.: Many robots make short work: report of the SRI international mobile robot team. AI Mag. 18(1), 55 (1997)

    Google Scholar 

  2. Arai, T., Pagello, E., Parker, L.E.: Guest editorial: advances in multirobot systems. IEEE Trans. Robot. Autom. 18(5), 655–661 (2002)

    Article  Google Scholar 

  3. Tang, F., Parker, L.E.: ASyMTRe: automated synthesis of multi-robot task solutions through software reconfiguration. In: IEEE international conference on robotics and automation, Barcelona, Spain (2005)

  4. Parker, J., Nunes, E., Godoy, J., Gini, M.: Exploiting spatial locality and heterogeneity of agents for search and rescue teamwork. J. Field Robot. 33(7), 877–900 (2016)

    Article  Google Scholar 

  5. Yan, Z., Jouandeau, N., Cherif, A.A.: A survey and analysis of multi-robot coordination. Int. J. Adv. Robot. Syst. 10(12), 399 (2013)

    Article  Google Scholar 

  6. Khamis, A., Hussein, A., Elmogy, A.: Multi-robot task allocation: a review of the state-of-the-art. In: Cooperative robots and sensor networks, pp. 31–51. Springer (2015)

  7. Gerkey, B.P., Matarić, M.J.: A formal analysis and taxonomy of task allocation in multi-robot systems. Int. J. Robot. Res. 23(9), 939–954 (2004)

    Article  Google Scholar 

  8. Nanjanath, M., Gini, M.: Dynamic task allocation for robots via auctions. In: IEEE international conference on robotics and automation, Orlando, FL, USA (2006)

  9. Wei, C., Hindriks, K.V., Jonker, C.M.: Dynamic task allocation for multi-robot search and retrieval tasks. Appl. Intell. 45(2), 383–401 (2016)

    Article  Google Scholar 

  10. Talebpour, Z., Martinoli, A.: Multi-robot coordination in dynamic environments shared with humans. In: IEEE international conference on robotics and automation, Brisbane, Australia (2018)

  11. Smith, R.G.: The contract net protocol: high-level communication and control in a distributed problem solver. IEEE Trans. Comput. C-29(12), 1104–1113 (1980)

    Article  Google Scholar 

  12. Guerrero, J., Oliver, G.: A multi-robot auction method to allocate tasks with deadlines. In: IFAC symposium on intelligent autonomous vehicles, Lecce, Italy (2010)

  13. Luo, L., Chakraborty, N., Sycara, K.: Distributed algorithms for multirobot task assignment with task deadline constraints. IEEE Trans. Autom. Sci. Eng. 12(3), 876–888 (2015)

    Article  Google Scholar 

  14. Choi, Y., Choi, Y., Briceno, S., Mavris, D.N.: Energy-constrained multi-UAV coverage path planning for an aerial imagery mission using column generation. J. Intell. Robot. Syst. 97(1), 125–139 (2020)

    Article  Google Scholar 

  15. Sujit P.B., Sousa, J.B.: Multi-UAV task allocation with communication faults. In: American control conference, Montreal, Canada (2012)

  16. Lozenguez, G., Mouaddib, A.-I., Beynier, A., Adouane, L., Martinet, P.: Simultaneous auctions for “Rendez-Vous” coordination phases in multi-robot multi-task mission. In: IEEE/WIC/ACM international joint conferences on web intelligence and intelligent agent technologies, Atlanta, GA, USA (2013)

  17. Heap, B., Pagnucco, M.: Repeated auctions for reallocation of tasks with pickup and delivery upon robot failure. In: International conference on principles and practice of multi-agent systems, Dunedin, New Zealand (2013)

  18. Khan, M.T., De Silva, C.W.: Autonomous and market-based fault tolerant algorithms for multi-robot cooperation. J. Inf. Sci. Eng. 30(2), 483–500 (2014)

    Google Scholar 

  19. Bernardine Dias, M., Zlot, R., Kalra, N., Anthony, S.: Market-based multirobot coordination: a survey and analysis. Proc. IEEE 94(7), 1257–1270 (2006)

    Article  Google Scholar 

  20. Ahmad, A., Babar, M.A.: Software architectures for robotic systems: a systematic mapping study. J. Syst. Softw. 122, 16–39 (2016)

    Article  Google Scholar 

  21. Portugal, D., Rocha, R.: A survey on multi-robot patrolling algorithms. In: Doctoral conference on computing, electrical and industrial systems, Costa de Caparica, Portugal (2011)

  22. Senanayake, M., Senthooran, I., Barca, J.C., Chung, H., Kamruzzaman, J., Murshed, M.: Search and tracking algorithms for swarms of robots: a survey . Robot. Autonomous Syst. 75(B), 422–434 (2016)

    Article  Google Scholar 

  23. Robin, C., Lacroix, S.: Multi-robot target detection and tracking: taxonomy and survey. Auton. Robot. 40(4), 729–760 (2016)

    Article  Google Scholar 

  24. Ayorkor Korsah, G., Stentz, A., Bernardine Dias, M.: A comprehensive taxonomy for multi-robot task allocation. Int. J. Robot. Res. 32(12), 1495–1512 (2013)

    Article  Google Scholar 

  25. Jiang, Y.: A survey of task allocation and load balancing in distributed systems. IEEE Trans. Parallel Distributed Syst. 27(2), 585–599 (2015)

    Article  Google Scholar 

  26. Nunes, E., Manner, M., Mitiche, H., Gini, M.: A taxonomy for task allocation problems with temporal and ordering constraints. Robot. Auton. Syst. 90, 55–70 (2017)

    Article  Google Scholar 

  27. Petersen, K., Feldt, R., Mujtaba, S., Mattsson, M.: Systematic mapping studies in software engineering. In: International conference on evaluation and assessment in software engineering, Bari, Italy (2008)

  28. Jia, X., Meng, M.Q.-H.: A survey and analysis of task allocation algorithms in multi-robot systems. In: IEEE international conference on robotics and biomimetics, Shenzhen, China (2013)

  29. Wohlin, C.: Guidelines for snowballing in systematic literature studies and a replication in software engineering. In: International conference on evaluation and assessment in software engineering, London, UK (2014)

  30. Hoeing, M., Dasgupta, P., Petrov, P., O’Hara, S.: Auction-based multi-robot task allocation in COMSTAR. In: International joint conference on autonomous agents and multiagent systems, Honolulu, Hawai’i (2007)

  31. Jouandeau, N., Yan, Z.: Improved trade-based multi-robot coordination. In: IEEE joint international information technology and artificial intelligence conference, Chongqing, China (2011)

  32. Yun, H., Li, Q., Jiang, D., Liu, H., Mao, S., Li, Y.: A contract net protocol based on information intermediary service in multi-agent system. In: International conference on artificial intelligence and computational intelligence, Shangai, China (2009)

  33. Kensler, J.A., Agah, A.: Neural networks-based adaptive bidding with the contract net protocol in multi-robot systems. Appl. Intell. 31, 347 (2009)

    Article  Google Scholar 

  34. Mohammad, N., Muhammad, S., Al-Mouhamed, M.: Design and implementation of reliable auctioning algorithms for multi-robot systems. In: International conference on advances in computing, communications and informatics, Mysore, India (2013)

  35. Sheng, W., Yang, Q., Tan, J., Xi, N.: Distributed multi-robot coordination in area exploration. Robot. Auton. Syst. 54(12), 945–955 (2006)

    Article  Google Scholar 

  36. Lujak, M., Giordani, S.: On the communication range in auction-based multi-agent target assignment. In: International workshop on self-organizing systems, Karlsruhe, Germany (2011)

  37. Wang, X., Sheng, B.: Multi-robot task allocation algorithm based on anxiety model and modified contract network protocol. In: IEEE information technology, networking, electronic and automation control conference, Chengdu, China (2017)

  38. Karmani, R.K., Latvala, T., Agha, G.: On scaling multi-agent task reallocation using market-based approach. In: International conference on self-adaptive and self-organizing systems, Boston, MA, USA (2007)

  39. Duvallet, F., Stentz, A.: Imitation learning for task allocation. In: IEEE/RSJ international conference on intelligent robots and systems, Taipei, Taiwan (2010)

  40. Ham, M., Agha, G.: A study of coordinated dynamic market-based task assignment in massively multi-agent systems. In: International conference on autonomous agents and multiagent systems, workshop on massively multi-agent technology, Honolulu, Hawai’i (2007)

  41. Dasgupta, P., Hoeing, M.: Dynamic pricing algorithms for task allocation in multi-agent swarms. In: International conference on autonomous agents and multiagent systems, workshop on massively multi-agent technology, Honolulu, Hawai’i (2007)

  42. Li, G., Tamura, Y., Wu, M., Yamashita, A., Asama, H.: Hybrid dynamic mobile task allocation and reallocation methodology for distributed multi-robot coordination. In: IEEE/ASME international conference on advanced intelligent mechatronics, Kaohsiung, Taiwan (2012)

  43. Fauadi, M.H.F.B.M., Lin, H., Murata, T.: Dynamic task assignment of autonomous AGV system based on multi agent architecture. In: IEEE international conference on progress in informatics and computing, Shangai, China (2010)

  44. Ferri, G., Munafo, A., Tesei, A., LePage, K.: A market-based task allocation framework for autonomous underwater surveillance networks. In: OCEANS, Aberdeen, UK (2017)

  45. Mezei, I., Malbasa, V., Stojmenovic, I.: Auction aggregation protocols for wireless robot-robot coordination. In: International conference on ad-hoc networks and wireless, Murcia, Spain (2009)

  46. Madhyastha, M., Reddy, S.C., Rao, S.: Online scheduling of a fleet of autonomous vehicles using agent-based procurement auctions. In: IEEE international conference on service operations and logistics, and informatics, Bari, Italy (2017)

  47. Ahmed, S., Nahrstedt, K., Wang, G.: Topology-aware optimal task allocation for mission critical environment - a decentralized approach. In: IEEE military communications conference, Baltimore, MD, USA (2011)

  48. Ahmed, S., Pongthawornkamol, T., Nahrstedt, K., Caesar, M., Wang, G.: Topology-aware optimal task allocation for publish/subscribe-based mission critical environment. In: IEEE military communications conference, Boston, MA, USA (2009)

  49. Viguria, A., Maza, I., Ollero, A.: Distributed service-based cooperation in aerial/ground robot teams applied to fire detection and extinguishing missions. Adv. Robot. 24(1-2), 1–23 (2010)

    Article  Google Scholar 

  50. Trigui, S., Koubaa, A., Cheikhrouhou, O., Youssef, H., Bennaceur, H., Sriti, M.-F., Javed, Y.: A distributed market-based algorithm for the multi-robot assignment problem. Proc. Comput. Sci. 32, 1108–1114 (2014)

    Article  Google Scholar 

  51. Cao, L., Shun Tan, H., Peng, H., Cong Pan, M.: Multiple UAVs hierarchical dynamic task allocation based on PSO-FSA and decentralized auction. In: IEEE international conference on robotics and biomimetics, Bali, Indonesia (2014)

  52. Bai, X., Yan, W., Cao, M., Xue, D.: Distributed multi-vehicle task assignment in a time-invariant drift field with obstacles. IET Contr. Theory Appl. 13(17), 2886–2893 (2019)

    Article  MathSciNet  Google Scholar 

  53. Bai, X., Yan, W., Cao, M., Xue, D.: Heterogeneous multi-vehicle task assignment in a time-invariant drift field with obstacles. In: 2017 IEEE 56th annual conference on decision and control (CDC), pp. 307–312. IEEE (2017)

  54. Amstutz, P., Correll, N., Martinoli, A.: Distributed boundary coverage with a team of networked miniature robots using a robust market-based algorithm. Ann. Math. Artif. Intell. 52(2-4), 307–333 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  55. Koenig, S., Tovey, C., Lagoudakis, M., Markakis, V., Kempe, D., Keskinocak, P., Kleywegt, A., Meyerson, A., Jain, S.: The power of sequential single-item auctions for agent coordination. In: AAAI, pp. 1625–1629 (2006)

  56. Sujit, P.B., Beard, R.: Distributed sequential auctions for multiple UAV task allocation. In: American control conference, New York, NY, USA (2007)

  57. Lee, D.-H., Zaheer, S.A., Kim, J.-H.: A resource-oriented, decentralized auction algorithm for multirobot task allocation. IEEE Trans. Autom. Sci. Eng. 12(4), 1469–1481 (2014)

    Article  Google Scholar 

  58. Lee, D.-H.: Resource-based task allocation for multi-robot systems. Robot. Auton. Syst. 103, 151–161 (2018)

    Article  Google Scholar 

  59. Williams, R.K., Gasparri, A., Ulivi, G.: Decentralized matroid optimization for topology constraints in multi-robot allocation problems. In: IEEE international conference on robotics and automation, Singapore (2017)

  60. Öztürk, S., Kuzucuoğlu, A.E.: Optimal bid valuation using path finding for multi-robot task allocation. J. Intell. Manuf., vol. 26 (2015)

  61. Farinelli, A., Iocchi, L., Nardi, D.: Distributed on-line dynamic task assignment for multi-robot patrolling. Auton. Robot. 41(6), 1321–1345 (2017)

    Article  Google Scholar 

  62. Jones, E.G., Dias, M.B., Stentz, A.: Learning-enhanced market-based task allocation for oversubscribed domains. In: IEEE/RSJ international conference on intelligent robots and systems, San Diego, CA, USA (2007)

  63. Talebpour, Z., Savare, S., Martinoli, A.: Market-based coordination in dynamic environments based on the Hoplites framework. In: IEEE/RSJ international conference on intelligent robots and systems, Vancouver, Canada (2017)

  64. Schneider, E., Sklar, E.I., Parsons, S., Özgelen, A.T.: Auction-based task allocation for multi-robot teams in dynamic environments. In: Towards autonomous robotic systems conference, Liverpool, UK (2015)

  65. Otte, M., Kuhlman, M.J., Sofge, D.: Auctions for multi-robot task allocation in communication limited environments. Auton. Robot. 44(3), 547–584 (2020)

    Article  Google Scholar 

  66. Viguria, A., Maza, I., Ollero, A.: SET: an algorithm for distributed multirobot task allocation with dynamic negotiation based on task subsets. In: IEEE international conference on robotics and automation, Rome, Italy (2007)

  67. Shoman, W., El-Barrawy, M., Mekhail, Y., Bahgat, A.B., Morgan, E.-S.I.: Introducing various novel optimization techniques for task allocation in multi-vehicles systems. In: IEEE international conference of vehicular electronics and safety, Cairo, Egypt (2019)

  68. Gautam, A., Thakur, A., Dhanania, G., Mohan, S.: A distributed algorithm for balanced multi-robot task allocation. In: International conference on industrial and information systems, Roorkee, India (2016)

  69. Khamis, A.M., Elmogy, A.M., Karray, F.O.: Complex task allocation in mobile surveillance systems. J. Intell. Robot. Syst. 64(1), 33–55 (2011)

    Article  Google Scholar 

  70. Liu, Y., Yang, J., Zheng, Y., Wu, Z., Yao, M.: Multi-robot coordination in complex environment with task and communication constraints. Int. J. Adv. Robot. Syst. 10(5), 229 (2013)

    Article  Google Scholar 

  71. Choi, H.-L., Brunet, L., How, J.P.: Consensus-based decentralized auctions for robust task allocation. IEEE Trans. Robot. 25(4), 912–926 (2009)

    Article  Google Scholar 

  72. 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, 97–123 (2017)

    Article  Google Scholar 

  73. Segui-Gasco, P., Shin, H.-S., Tsourdos, A., Segui, V.J.: A combinatorial auction framework for decentralised task allocation. In: IEEE globecom workshops, Austin, TX, USA (2014)

  74. Das, G.P., McGinnity, T.M., Coleman, S.A., Behera, L.: A distributed task allocation algorithm for a multi-robot system in healthcare facilities. J. Intell. Robot. Syst. 80(1), 33–58 (2015)

    Article  Google Scholar 

  75. Ye, F., Chen, J., Sun, Q., Tian, Y., Jiang, T.: Decentralized task allocation for heterogeneous multi-uav system with task coupling constraints. J. Supercomput. 77, 111–132 (2021)

    Article  Google Scholar 

  76. Zhao, W., Meng, Q., Chung, P.W.H.: A heuristic distributed task allocation method for multivehicle multitask problems and its application to search and rescue scenario. IEEE Trans. Cybern. 46(4), 902–915 (2015)

    Article  Google Scholar 

  77. Chen, X., Zhang, P., Du, G., Li, F.: A distributed method for dynamic multi-robot task allocation problems with critical time constraints. Robot. Auton. Syst. 118, 31–46 (2019)

    Article  Google Scholar 

  78. Zhou, L., Shi, Y., Wang, J., Yang, P.: A balanced heuristic mechanism for multirobot task allocation of intelligent warehouses. Math. Probl. Eng., vol. 2014 (2014)

  79. Basile, F., Chiacchio, P., Marino, E.D.: Auction-based mechanisms for the control of vehicles in smart logistic systems. In: IEEE international conference on emerging technologies and factory automation, Zaragoza, Spain (2019)

  80. Choi, S.H., Zhu, W.K.: Performance optimisation of mobile robots in dynamic environments. In: IEEE international conference on virtual environments human-computer interfaces and measurement systems, pp. 54–59, Tianjin, China (2012)

  81. Wan, W., Zhang, J., Wang, M.: A multi-agent negotiation protocol based on extended case based reasoning. In: International conference on fuzzy systems and knowledge discovery, Haikou, China (2007)

  82. Zhang, K., Collins, E.G., Barbu, A.: An efficient stochastic clustering auction for heterogeneous robotic collaborative teams. J. Intell. Robot. Syst. 72(3-4), 541–558 (2013)

    Article  Google Scholar 

  83. Poulet, C., Corruble, V., Seghrouchni, A.E.F.: Auction-based strategies for the open-system patrolling task. In: International conference on principles and practice of multi-agent systems, Kuching, Malaysia (2012)

  84. Nanjanath, M., Gini, M.: Repeated auctions for robust task execution by a robot team. Robot. Auton. Syst. 58(7), 900–909 (2010)

    Article  Google Scholar 

  85. Kalra, N., Martinoli, A.: Comparative study of market-based and threshold-based task allocation. In: International symposium on distributed autonomous robotic systems, Minneapolis, MN, USA (2006)

  86. Badreldin, M., Hussein, A., Khamis, A.: A comparative study between optimization and market-based approaches to multi-robot task allocation. Adv. Artif. Intell. 2013, 12 (2013)

    Article  Google Scholar 

  87. Zhang, D., Schneider, E., Sklar, E.: A cross-landscape evaluation of multi-robot team performance in static task-allocation domains. In: Towards autonomous robotic systems conference (2019)

  88. Nayak, S., Yeotikar, S., Carrillo, E., Rudnick-Cohen, E., Jaffar, M.K.M., Patel, R., Azarm, S., Herrmann, J.W., Xu, H., Otte, M.: Experimental comparison of decentralized task allocation algorithms under imperfect communication. IEEE Robot. Autom. Lett. 5(2), 572–579 (2020)

    Article  Google Scholar 

  89. Johnson, L., Ponda, S., Choi, H.-L., How, J.: Asynchronous decentralized task allocation for dynamic environments. In: Infotech@Aerospace, St. Louis, MO, USA (2011)

  90. Ismail, S., Sun, L.: Decentralized hungarian-based approach for fast and scalable task allocation. In: International conference on unmanned aircraft systems, Miami, FL, USA (2017)

  91. Johnson, L., Choi, H.-L., How, J.P.: The hybrid information and plan consensus algorithm with imperfect situational awareness. In: International symposium on distributed autonomous robotic systems, London, UK (2016)

  92. Lagoudakis, M.G., Berhault, M., Koenig, S., Keskinocak, P., Kleywegt, A.J.: Simple auctions with performance guarantees for multi-robot task allocation. In: IEEE/RSJ international conference on intelligent robots and systems, Sendai, Japan (2004)

  93. Umbrico, A., Orlandini, A., Cesta, A.: An ontology for human-robot collaboration. Procedia CIRP 93, 1097–1102 (2020)

    Article  Google Scholar 

  94. Bonsignorio, F.: A new kind of article for reproducible research in intelligent robotics [From the field]. IEEE Robot. Autom. Mag. 24(3), 178–182 (2017)

    Article  Google Scholar 

  95. Koubâa, A., Cheikhrouhou, O., Bennaceur, H., Sriti, M.-F., Javed, Y., Ammar, A.: Move and improve: a market-based mechanism for the multiple depot multiple travelling salesmen problem. J. Intell. Robot. Syst. 85(2), 307–330 (2017)

    Article  Google Scholar 

Download references

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Author notes

    Authors and Affiliations

    1. Information Processing and Systems Department, ONERA, The French Aerospace Lab, 1, Avenue Edouard Belin, Toulouse, 31400, France

      Félix Quinton, Christophe Grand & Charles Lesire

    Authors
    1. Félix Quinton
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Christophe Grand
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Charles Lesire
      View author publications

      You can also search for this author in PubMed Google Scholar

    Contributions

    • Félix Quinton: Design of the primary studies selection and classification processes ; Selection, classification and analysis of primary studies ; redaction of the first draft of the manuscript.

    • Christophe Grand: Design of the primary studies selection and classification processes ; Selection, classification and analysis of primary studies ; commented on previous versions of the manuscript ; read and approved the final manuscript.

    • Charles Lesire: Design of the primary studies selection and classification processes ; Selection, classification and analysis of primary studies ; commented on previous versions of the manuscript ; read and approved the final manuscript.

    Corresponding author

    Correspondence to Félix Quinton.

    Ethics declarations

    Conflict of Interests

    The authors have no relevant financial or non-financial interests to disclose.

    Additional information

    Publisher’s Note

    Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

    Christophe Grand and Charles Lesire contributed equally to this work.

    Rights and permissions

    Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

    Reprints and permissions

    About this article

    Check for updates. Verify currency and authenticity via CrossMark

    Cite this article

    Quinton, F., Grand, C. & Lesire, C. Market Approaches to the Multi-Robot Task Allocation Problem: a Survey. J Intell Robot Syst 107, 29 (2023). https://doi.org/10.1007/s10846-022-01803-0

    Download citation

    • Received:

    • Accepted:

    • Published:

    • DOI: https://doi.org/10.1007/s10846-022-01803-0

    Keywords

    Search

    Navigation