Multi-robot Task Allocation: A Review of the State-of-the-Art

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 604)

Abstract

Multi-robot systems (MRS) are a group of robots that are designed aiming to perform some collective behavior. By this collective behavior, some goals that are impossible for a single robot to achieve become feasible and attainable. There are several foreseen benefits of MRS compared to single robot systems such as the increased ability to resolve task complexity, increasing performance, reliability and simplicity in design. These benefits have attracted many researchers from academia and industry to investigate how to design and develop robust versatile MRS by solving a number of challenging problems such as complex task allocation, group formation, cooperative object detection and tracking, communication relaying and self-organization to name just a few. One of the most challenging problems of MRS is how to optimally assign a set of robots to a set of tasks in such a way that optimizes the overall system performance subject to a set of constraints. This problem is known as Multi-robot Task Allocation (MRTA) problem. MRTA is a complex problem especially when it comes to heterogeneous unreliable robots equipped with different capabilities that are required to perform various tasks with different requirements and constraints in an optimal way. This chapter provides a comprehensive review on challenging aspects of MRTA problem, recent approaches to tackle this problem and the future directions.

References

  1. 1.
    Yi-Lin, L., Kuo-Lan, S.: Multi-robot-based intelligent security system. Artif. Life Robot. 16, 137–141 (2011)CrossRefGoogle Scholar
  2. 2.
    Nagatani, K., Okada, Y., Tokunaga, N., Kiribayashi, S., Yoshida, K., Ohno, K., Koyanagi, E., et al.: Multi-robot exploration for search and rescue missions. In: IEEE International Workshop on Safety, Security and Rescue Robotics (SSRR), pp. 373–387 (2009)Google Scholar
  3. 3.
    Marino, A., Parker, L.E., Antonelli, G., Caccavale, F.: A decentralized architecture for multi-robot systems based on the null-space-behavioral control with application to multi-robot border patrolling. J. Intell. Robot. Syst. 71, 423–444 (2013)CrossRefGoogle Scholar
  4. 4.
    Khamis, A., ElGindy, A.: Minefield mapping using cooperative multirobot systems. J. Robot. 2012 (2012)Google Scholar
  5. 5.
    Espina, M.V., Grech, R., De Jager, D., Remagnino, P., Iocchi, L., Marchetti, L., King, C., et al.: Multi-robot teams for environmental monitoring. Innovations Defence Support Syst. 336, 183–209 (2011)Google Scholar
  6. 6.
    Shkurti, F., Xu, A., Meghjani, M., Gamboa Higuera, J.C., Girdhar, Y., Giguere, P., Dudek, G., et al.: Multi-domain monitoring of marine environments using a heterogeneous robot team. Intell. Robots Syst. (IROS) 1747–1753 (2012)Google Scholar
  7. 7.
    Shiomi, M., Kamei, K., Kondo, T., Miyashita, T., Hagita, N.: Robotic service coordination for elderly people and caregivers with ubiquitous network robot platform. In: IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO) (2013)Google Scholar
  8. 8.
    Lerman, K., Jones, C., Galstyan, A., Mataric, M.: Analysis of dynamic task allocation in multi-robot systems. Int. J. Robot. Res. 25, 225–241 (2006)CrossRefGoogle Scholar
  9. 9.
    Tang, F., Parker, L.E.: A complete methodology for generating multi-robot task solutions using asymtre-d and market-based task allocation. In: International Conference on Robotics and Automation, IEEE, pp. 3351–3358 (2007)Google Scholar
  10. 10.
    Gerkey, B., Mataric, M.: A framework for studying multi-robot task allocation (2003)Google Scholar
  11. 11.
    Korsah, G.A., Stentz, A., Dias, M.B.: A comprehensive taxonomy for multi-robot task allocation. Int. J. Robot. Res. 32, 1495–1513 (2013)CrossRefGoogle Scholar
  12. 12.
    Dias, M.B.: Trader-bots: a new paradigm for robust and efficient multirobot coordination in dynamic environments. Ph.D. Thesis, Robotics Institute, Carnegie Mellon University (2004)Google Scholar
  13. 13.
    Zlot, R., Stentz, A.: Market-based multirobot coordination for complex tasks. Int. J. Robot. Res. 73–101 (2006)Google Scholar
  14. 14.
    de Longueville, M.: A Course in Topological Combinatorics. Springer, New York (2012)Google Scholar
  15. 15.
    Vatsolaki, P., Tsalpatouros, A.: Ewos: A sealed-bid auction system design and implementation for electricity interconnector capacity allocation. In: Information, Intelligence, Systems and Applications (IISA) (2013)Google Scholar
  16. 16.
    Kim, D., So, Y., Kim, S.: Study of marker array list method for augmented reality service based smart home. Int. J. Smart Home 5, 51–64 (2011)Google Scholar
  17. 17.
    Higuera, J., Dudek, G.: Fair subdivision of multi-robot tasks. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 3014–3019 (2013)Google Scholar
  18. 18.
    Kuhn, H.W.: The hungarian method for the assignment problem. Nav. Res. Logistics Quart. 2, 83–97 (1955)Google Scholar
  19. 19.
    Nam, C., Shell, D.: Assignment algorithms for modeling resource contention and interference in multi-robot task-allocation. In: Presentation at IEEE International Conference on Robotics and Automation (ICRA) (2014)Google Scholar
  20. 20.
    Lattarulo, V., Parks, G.T.: A preliminary study of a new multi-objective optimization algorithm. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2012)Google Scholar
  21. 21.
    Xu, Z., Wen, Q.: Approximation hardness of min-max tree covers. Oper. Res. Lett. 38, 169–173 (2010)Google Scholar
  22. 22.
    Sarin, S.C., Sherali, H.D., Bhootra, A.: New tighter polynomial length formulations for the asymmetric traveling salesman problem with and without precedence constraints. Oper. Res. Lett. 33, 62–70 (2005)Google Scholar
  23. 23.
    Bektas, T.: The multiple traveling salesman problem: an overview of formulations and solution procedures. In: Omega, vol. 34, pp. 209–219. Elsevier (2006)Google Scholar
  24. 24.
    Hussein, A., Khamis, A.: Market-based approach to multi-robot task allocation. In: International Conference on Individual and Collective Behaviors in Robotics (ICBR), IEEE (2013)Google Scholar
  25. 25.
    Dasgupta, P.: Multi-robot task allocation for performing cooperative foraging tasks in an initially unknown environment. Innovations Defence Support Syst., 338, 5–20 (2011)Google Scholar
  26. 26.
    Khamis, A.M., Elmogy, A.M., Karray, F.O.: Complex task allocation in mobile surveillance systems. J. Intell. Robot. Syst. 64, 33–55 (2011)Google Scholar
  27. 27.
    Aylett, R., Barnes, D.: A multi-robot architecture for planetary rovers. In: Proceedings of the 5th ESA Workshop on Advanced Space Technologies for Robotics and Automation, pp. 1–3 (1998)Google Scholar
  28. 28.
    Botelho, S.C., Alami, R.: M+: a scheme for multi-robot cooperation through negotiated task allocation and achievement. In: International Conference on Robotics and Automation, pp. 1234–1239 (1999)Google Scholar
  29. 29.
    Horling, B., Lesser, V.: A survey of multi-agent organizational paradigms. Knowl. Eng. Rev. 19, 281–316 (2004)Google Scholar
  30. 30.
    Brumitt, B.L., Stentz, A.: Grammps: a generalized mission planner for multiple mobile robots in unstructured environments. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1564–1571 (1998)Google Scholar
  31. 31.
    Al-Yafi, K., Lee, H., Mansouri, A.: Mtap-masim: a multi-agent simulator for the mobile task allocation problem. In: IEEE International Workshop on Enabling Technologies: Infrastructures for Collaborative Enterprises, pp. 25–27 (2009)Google Scholar
  32. 32.
    Coltin, B., Veloso, M.: Mobile robot task allocation in hybrid wireless sensor networks. In: Intelligent Robots and Systems (IROS), pp. 2932–2937 (2010)Google Scholar
  33. 33.
    Liu, C., Kroll, A.: A centralized multi-robot task allocation for industrial plant inspection by using a* and genetic algorithms. Artif. Intell. Soft Comput. 466–474 (2012)Google Scholar
  34. 34.
    Giordani, S., Lujak, M., Martinelli, F.: A distributed algorithm for the multi-robot task allocation problem. In: Trends Applied Intelligent System, Springer, pp. 721–730 (2010)Google Scholar
  35. 35.
    Han-Lim, C., Brunet, L., How, J.: Consensus-based decentralized auctions for robust task allocation In: IEEE Transactions on Robotics, pp. 912–926 (2009)Google Scholar
  36. 36.
    Ping-an, G., Zi-xing, C., Ling-li, Y.: Evolutionary computation approach to decentralized multi-robot task allocation. In: International Conference on Natural Computation (ICNC), pp. 415–419 (2009)Google Scholar
  37. 37.
    Elmogy, A.: Market-based framework for mobile surveillance systems. Ph.D. Thesis, University of Waterloo (2010)Google Scholar
  38. 38.
    Zlot, R.M.: An auction-based approach to complex task allocation for multirobot teams. Ph.D. Thesis, Carnegie Mellon University (2006)Google Scholar
  39. 39.
    Smith, R.: Communication and control in problem solver. In: IEEE Transactions on Computers (1980)Google Scholar
  40. 40.
    Alibhai, Z.: What is contract net interaction protocol? IRMS Laboratory, SFU (2003)Google Scholar
  41. 41.
    Dias, M., Stentz, A.: Opportunistic optimization for market-based multirobot control. In: International Conference on Intelligent Robots and Systems, pp. 2714–2720 (2002)Google Scholar
  42. 42.
    Badreldin, M., Hussein, A., Khamis, A.: A comparative study between optimization and market-based approaches to multi-robot task allocation. Adv. Artif. Intell. 2013, 1–11 (2013)Google Scholar
  43. 43.
    Dias, M.B., Stentz, A.: A free market architecture for distributed control of a multirobot system. In: International Conference on Intelligent Autonomous Systems, pp. 115–122 (2000)Google Scholar
  44. 44.
    Zlot, R., Stentz, A., Dias, M.B., Thayer, S.: Multi-robot exploration controlled by a market economy. In: International Conference on Robotics and Automation (ICRA) (2002)Google Scholar
  45. 45.
    Dias, M., Zlot, R., Kalra, N., Stentz, A.: Market-based multirobot coordination: a survey and analysis. In: Proceedings of the IEEE, pp. 1257–1270 (2006)Google Scholar
  46. 46.
    Horst, R., Pardalos, P.M., Thoai, N.V.: Introduction to Global Optimization: Nonconvex Optimization and Its Applications. Kluwer Academic Publishers, Dordrecht (2000)Google Scholar
  47. 47.
    Spall, J.C.: Stochastic optimization. In: Handbook of Computational Statistics, Springer, New York, pp. 173–201 (2012)Google Scholar
  48. 48.
    Diwekar, U.: Optimization under uncertainty. In: Introduction to Applied Optimization. Springer, New York, pp. 1–54 (2008)Google Scholar
  49. 49.
    Lenagh, W.H.: Multi-robot task allocation: a spatial queuing approach. Ph.D. dissertation, University of Nebraska, Omaha (2013)Google Scholar
  50. 50.
    Khamis, A.: Ece457a: Cooperative and Adaptive Algorithms. University of Waterloo. Springer, Canada (2014)Google Scholar
  51. 51.
    Atay, N., Bayazit, B.: Mixed-integer linear programming solution to multi-robot task allocation problem. Washington Univ. St. Louis, Tech. Rep. (2006)Google Scholar
  52. 52.
    Darrah, M., Niland, W., Stolarik, B.M.: Multiple uav dynamic task allocation using mixed integer linear programming in a sead mission. In: American Institute of Aeronautics and Astronautics, pp. 2324–2334 (2005)Google Scholar
  53. 53.
    Mosteo, A.R., Montano, L.: Simulated annealing for multi-robot hierarchical task allocation with flexible constraints and objective functions. In: Workshop on Network Robot Systems Toward Intelligent Robotic Systems Integrated with Environments (2006)Google Scholar
  54. 54.
    Mosteo, A.R.: Multi-robot task allocation for service robotics: from unlimited to limited communication range. Ph.D. Thesis, Universidad de Zaragoza (2010)Google Scholar
  55. 55.
    Juedes, D., Drews, F., Welch, L., Fleeman, D.: Heuristic resource allocation algorithms for maximizing allowable workload in dynamic, distributed real-time systems. In: Parallel and Distributed Processing Symposium, pp. 1631–1638 (2004)Google Scholar
  56. 56.
    Kmiecik, W., Wojcikowski, M., Koszalka, L., Kasprzak, A.: Task allocation in mesh connected processors with local search meta-heuristic algorithms. In: Intelligent Information and Database Systems, Springer, pp. 215–224 (2010)Google Scholar
  57. 57.
    Shea, P.J., d Alexander, K., Peterson, J.: Group tracking using genetic algorithms. In: Proceedings of the International Society Information Fusion (2003)Google Scholar
  58. 58.
    Jones, E.G., Dias, M., Stentz, A.: Time-extended multi-robot coordination for domains with intra-path constraints. Auton. Robots 59, 41–56 (2011)Google Scholar
  59. 59.
    Wang, J., Gu, Y., Li, X.: Multi-robot task allocation based on ant colony algorithm. J. Comput. 7, 2160–2167 (2012)Google Scholar
  60. 60.
    Ding, Y., He, Y., Jiang, J.: Multi-robot cooperation method based on the ant algorithm. In: IEEE Swarm Intelligence Symposium, pp. 14–18 (2003)Google Scholar
  61. 61.
    Chen, W., Lin, C.: A hybrid heuristic to solve a task allocation problem. Comput. Oper. Res. 27, 287–303 (2000)Google Scholar
  62. 62.
    Liu, D.K., Kulatunga, A.K.: Simultaneous planning and scheduling for multi-autonomous vehicles. In: Evolutionary Scheduling, Springer, pp. 437–464 (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Engineering Science DepartmentSuez University Egypt and Vestec. Inc.WaterlooCanada
  2. 2.Intelligent Systems Lab (LSI) Research GroupUniversidad Carlos III de Madrid (UC3M)MadridSpain
  3. 3.Computers and Control Engineering DepartmentTanta University, Egypt and Arab East CollegesRiyadhKSA

Personalised recommendations