A Pricing Mechanism for Task Oriented Resource Allocation in Cloud Robotics

  • Lujia WangEmail author
  • Ming Liu
  • Max Q.-H. Meng
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 36)


Cloud robotics is currently driving interests in both academia and industry, especially for systems with limited computation capability. Resource allocation is the fundamental and dominant problem for resource sharing among agents in the cloud robotics system. This chapter introduces a novel resource allocation framework for cloud robotics and proposes a Stackelberg game model and the corresponding task oriented pricing mechanism for resource allocation. Simulation investigates the parameter selection and time cost of the proposed mechanism. Experimental results of co-localization task demonstrate that the proposed mechanism achieve an optimal performance in resource allocation.


Pricing algorithm Resource allocation Cloud robotics 



This work is supported by RGC GRF Grant CUHK14205914 awarded to Prof. Max Q.-H. Meng; partially supported the Research Grant Council of Hong Kong SAR Government, China, under project No. 16206014 and No. 16212815; National Natural Science Foundation of China No. 6140021318, awarded to Prof. Ming Liu.


  1. 1.
    Aldebaran Robotics: Nao robot,
  2. 2.
    An, B., Lesser, V.: Characterizing contract-based multiagent resource allocation in networks. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 40(3), 575–586 (2010)CrossRefGoogle Scholar
  3. 3.
    Arumugam, R., Enti, V., Bingbing, L., Xiaojun, W., Baskaran, K., Kong, F.F., Kumar, A., Meng, K.D., Kit, G.W.: DAvinCi: a cloud computing framework for service robots. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 3084–3089, May 2010Google Scholar
  4. 4.
    Berard, B., Petrie, C., Smith, N.: Quadrotor UAV interface and localization design. In: A Major Qualify Project of Missile Defense Agency under Air Force Contract F19628-00-C-0002 (1Apr00–31Mar05) (2010)Google Scholar
  5. 5.
    Berhault, M., Huang, H., Keskinocak, P., Koenig, S., Elmaghraby, W., Griffin, P., Kleywegt, A.: Robot exploration with combinatorial auctions. In: Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), vol. 2, pp. 1957–1962, October 2003Google Scholar
  6. 6.
    Bichier, M., Lin, K.J.: Service-oriented computing. Computer 39(3), 99–101 (2006)CrossRefGoogle Scholar
  7. 7.
    Brooks, A., Williams, S.: Tracking people with networks of heterogeneous sensors. In: Proceedings of the Australasian Conference on Robotics and Automation, pp. 1–7. Citeseer (2003)Google Scholar
  8. 8.
    Burkard, R.E., Dell’Amico, M., Martello, S., et al.: Assignment Problems. Revised Reprint, SIAM (2009)zbMATHCrossRefGoogle Scholar
  9. 9.
    Chang, M., He, J., Castro-Leon, E.: Service-orientation in the computing infrastructure. In: 2nd IEEE International Symposium on Service-Oriented System Engineering, Shanghai, China, pp. 27–33, October 2006Google Scholar
  10. 10.
    Chen, Y., Du, Z., García-Acosta, M.: Robot as a service in cloud computing. In: 2010 5th IEEE International Symposium on Service Oriented System Engineering, pp. 151–158, June 2010Google Scholar
  11. 11.
    Cheng, M.Y., Tran, D.H., Wu, Y.W.: Using a fuzzy clustering chaotic-based differential evolution with serial method to solve resource-constrained project scheduling problems. Autom. Constr. 37(0), 88–97 (2014),
  12. 12.
    Choi, H.L., Brunet, L., How, J.P.: Consensus-based decentralized auctions for robust task allocation. IEEE Trans. Robot. 25(4), 912–926 (2009)CrossRefGoogle Scholar
  13. 13.
    Clark, J.: Inside “indigo”—infrastructure for web services and connected applications. Microsoft Press (Apr 2005)Google Scholar
  14. 14.
    Colas, F., Mahesh, S., Pomerleau, F., Liu, M., Siegwart, R.: 3d path planning and execution for search and rescue ground robots. In: 2013 IEEE/RSJ InternationalConference on Intelligent Robots and Systems (IROS), pp. 722–727. IEEE (2013)Google Scholar
  15. 15.
    Corporation, M.: Microsoft robotics developer studio,
  16. 16.
    Darmann, A., Pferschy, U., Schauer, J.: Resource allocation with time intervals. Theor. Comput. Sci. 411(49), 4217–4234 (2010).
  17. 17.
    Dasarathy, B.: Decision Fusion, vol. 1994. IEEE Computer Society Press (1994)Google Scholar
  18. 18.
    Date, C., Darwen, H.: A Guide to the SQL Standard, vol. 3. Addison-Wesley Reading (1987)Google Scholar
  19. 19.
    Dias, M.B., Zlot, R., Kalra, N., Stentz, A.: Market-based multirobot coordination: a survey and analysis. Proc. IEEE 94(7), 1257–1270 (2006)CrossRefGoogle Scholar
  20. 20.
    Heinemann, F.C.: Web Programming with the Sap Web Applications Server. SAP Press (2003)Google Scholar
  21. 21.
    Gerkey, B.P., Mataric, M.J.: A formal analysis and taxonomy of task allocation in multi-robot systems. Int. J. Robot. Res. 23(9), 939–954 (2004).
  22. 22.
    Gostai Coop.: Gostai,
  23. 23.
    Hall, D., Llinas, J.: An introduction to multisensor data fusion. Proc. IEEE 85(1), 6–23 (1997)CrossRefGoogle Scholar
  24. 24.
    Higuera, J., Gamboa, C., Dudek, G.: Fair subdivision of multi-robot tasks. In: In: Proceedings of the 2013 IEEE International Conference on Robotics and Automation (ICRA 2013), pp. 2999-3004 (2013)Google Scholar
  25. 25.
    Hu, G., Tay, W.P., Wen, Y.: Cloud robotics: architecture, challenges and applications. Netw. IEEE 26(3), 21–28 (2012)CrossRefGoogle Scholar
  26. 26.
    Hunziker, D., Gajamohan, M., Waibel, M., D’Andrea, R.: Rapyuta: the RoboEarth cloud engine. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 438–444, May 2013Google Scholar
  27. 27.
    Intel Coop.: Service-oriented enterprise, the technology path to business transformation. (2 Oct 2005).
  28. 28.
    Jalaparti, V., Nguyen, G., Gupta, I., Caesar, M.: Cloud resource allocation games. Technical report, Department of Computer Science (2010)Google Scholar
  29. 29.
    Kaempchen, N., Dietmayer, K.: Data synchronization strategies for multi-sensor fusion. In: Proceedings of the IEEE Conference on Intelligent Transportation Systems. Citeseer (2003)Google Scholar
  30. 30.
    Kam, M., Zhu, X., Kalata, P.: Sensor fusion for mobile robot navigation. Proc. IEEE 85(1), 108–119 (1997)CrossRefGoogle Scholar
  31. 31.
    Kehoe, B., Matsukawa, A., Candido, S., Kuffner, J., Goldberg, K.: Cloud-based robot grasping with the Google object recognition engine. In: IEEE International Conference on Robotics and Automation (ICRA) (2013)Google Scholar
  32. 32.
    Kehoe, B., Warrier, D., Patil, S., Goldberg, K.: Cloud-based grasp analysis and planning for toleranced parts using parallelized Monte Carlo sampling. IEEE Trans. Autom. Sci. Eng. 12(2) (2015)Google Scholar
  33. 33.
    Klimentjew, D., Hendrich, N., Zhang, J.: Multi sensor fusion of camera and 3d laser range finder for object recognition. In: 2010 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 236–241. IEEE (2010)Google Scholar
  34. 34.
    Lagoudakis, M.G., Markakis, E., Kempe, D., Keskinocak, P.: Auction-based multi-robot routing. In: Proceedings of the International Conference on Robotics: Science and Systems (ROBOTICS), pp. 343–350 (2005)Google Scholar
  35. 35.
    Lemaire, T., Alami, R., Lacroix, S.: A distributed tasks allocation scheme in multi-UAV context. In: Proceedings of the 2004 IEEE International Conference on Robotics and Automation, ICRA’04, vol. 4, pp. 3622–3627. IEEE (2004)Google Scholar
  36. 36.
    Lin, L., Zheng, Z.: Combinatorial bids based multi-robot task allocation method. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation (ICRA 2005), pp. 1145–1150. IEEE (2005)Google Scholar
  37. 37.
    Lingzhi, L., Nilanjan, C., Sycara, K.: Distributed algorithm design for multi-robot task assignment with deadlines for tasks. In: IEEE International Conference on Robotics and Automation, (ICRA 2013), pp. 2992–2998 (2013)Google Scholar
  38. 38.
    Liu, M., Pradalier, C., Pomerleau, F., Siegwart, R.: Scale-only visual homing from an omnidirectional camera. In: 2012 IEEE International Conference on Robotics and Automation (ICRA), pp. 3944–3949. IEEE (2012)Google Scholar
  39. 39.
    Liu, M., Colas, F., Oth, L., Siegwart, R.: Incremental topological segmentation for semi-structured environments using discretized GVG. Auton. Robot. 38(2), 143–160 (2014)CrossRefGoogle Scholar
  40. 40.
    Liu, M., Siegwart, R.: DP-FACT: towards topological mapping and scene recognition with color for omnidirectional camera. In: 2012 IEEE International Conferenceon Robotics and Automation (ICRA), pp. 3503–3508. IEEE (2012)Google Scholar
  41. 41.
    Liu, M., Pradalier, C., Siegwart, R.: Visual homing from scale with an uncalibrated omnidirectional camera. IEEE Trans. Robot. 29(6), 1353–1365 (2013)CrossRefGoogle Scholar
  42. 42.
    Liu, M., Siegwart, R.: Information theory based validation for point-cloud segmentation aided by tensor voting. In: International Conference on Information and Automation (ICIA). IEEE (2013)Google Scholar
  43. 43.
    Liu, M., Siegwart, R.: Navigation on point-clouda—Riemannian metric approach. In: 2014 IEEE International Conference on Robotics and Automation(ICRA), pp. 4088–4093. IEEE (2014)Google Scholar
  44. 44.
    de Lope, J., Maravall, D., Quiñonez, Y.: Response threshold models and stochastic learning automata for self-coordination of heterogeneous multi-task distribution in multi-robot systems. Robot. Auton. Syst. 61(7), 714–720 (2013)CrossRefGoogle Scholar
  45. 45.
    Matti, P.: Nash and Stackelberg solutions in a differential game model of capitalism. J. Econ. Dyn. Control 6(0), 173–186 (1983), 0165-1889. doi: 10.1016/0165-1889(83)90048-9
  46. 46.
    McMurtry, C., Mercuri, M., Watling, N.: Microsoft Windows Communication Foundation: Hands-on. Sams Press (May25 2006)Google Scholar
  47. 47.
    Moshe Zadka, G.L.: The twisted network framework. (2010)
  48. 48.
    Mosteo, A.R., Montano, L.: A survey of multi-robot task allocation. Technical report, Instituto de Investigacin en Ingenierła de Aragn (I3A) (2010)Google Scholar
  49. 49.
    Mudholkar, G.S., Srivastava, D.K., Kollia, G.D.: A generalization of the weibull distribution with application to the analysis of survival data. J. Am. Stat. Assoc. 91(436), 1575–1583 (1996)zbMATHMathSciNetCrossRefGoogle Scholar
  50. 50.
    Nurmi, D., Wolski, R., Grzegorczyk, C., Obertelli, G., Soman, S., Youseff, L., Zagorodnov, D.: Eucalyptus: a technical report on an elastic utility computing architecture linking your programs to useful systems (2008)Google Scholar
  51. 51.
    OpenNebula Project:—the open source toolkit for cloud computing,
  52. 52.
    Piaggio, M., Zaccaria, R.: Distributing a robotic system on a network: the ETHNOS approach. Adv. Robot. 11(8), 743–758 (1998)CrossRefGoogle Scholar
  53. 53.
    Rai, A., Bhagwan, R., Guha, S.: Generalized resource allocation for the cloud. In: Proceedings of the 3rd Symposium on Cloud Computing (SOCC). San Jose, CA, October 2012Google Scholar
  54. 54.
    Riazuelo, L., Civera, J., Montiel, J.: \(C^2\) TAM: a cloud framework for cooperative tracking and mapping. Robot. Auton. Syst. 62(4), 401–413 (2014)CrossRefGoogle Scholar
  55. 55.
    Rimal, B.P., Choi, E., Lumb, I.: A taxonomy and survey of cloud computing systems. In: Fifth International Joint Conference on INC, IMS and IDC, 2009. NCM’09. pp. 44–51. IEEE (2009)Google Scholar
  56. 56.
    Rodriguez, M., Buyya, R.: Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE Trans. Cloud Comput. 2(2), 222–235 (2014)CrossRefGoogle Scholar
  57. 57.
    Steven, C.: Robots, incorporated. IEEE Spectrum (August 2007).
  58. 58.
    Sempolinski, P., Thain, D.: A comparison and critique of eucalyptus, opennebula and nimbus. In: 2010 IEEE Second International Conference on Cloud Computing Technology and Science (CloudCom), 30 2010-December 3 2010, pp. 417 –426Google Scholar
  59. 59.
    Sheth, A., Ranabahu, A.: Semantic modeling for cloud computing, part 1. Internet Comput. IEEE 14(3), 81–83 (2010)Google Scholar
  60. 60.
    Sung, C., Ayanian, N., Rus, D.: Improving the performance of multi-robot systems by task switching. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 2999–3006. IEEE (2013)Google Scholar
  61. 61.
    Tan, M., Wang, L., Tardioli, D., Liu, M.: A resource allocation strategy in a robotic ad-hoc network. In: 2014 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 122–127, May 2014Google Scholar
  62. 62.
    Thiruvady, D., Ernst, A., Singh, G.: Parallel ant colony optimization for resource constrained job scheduling. Ann. Oper. Res. 1–18 (2014).
  63. 63.
    Tsai, W., Huang, Q., Sun, X.: A collaborative service-oriented simulation framework with microsoft robotic studio. In: 41st Annual. Simulation Symposium, ANSS 2008, pp. 263–270, April 2008Google Scholar
  64. 64.
    Tsai, W., Sun, X., Huang, Q., Karatza, H.: An ontology-based collaborative service-oriented simulation framework with microsoft robotics studio. Simul. Model. Pract. Theory 16(9), 1392–1414 (2008)CrossRefGoogle Scholar
  65. 65.
    Viguria, A., Maza, I., Ollero, A.: Set: an algorithm for distributed multirobot task allocation with dynamic negotiation based on task subsets. In: 2007 IEEE International Conference on Robotics and Automation, pp. 3339–3344. IEEE (2007)Google Scholar
  66. 66.
    Viguria, A., Maza, I., Ollero, A.: S+T: an algorithm for distributed multirobot task allocation based on services for improving robot cooperation. In: IEEE International Conference on Robotics and Automation, ICRA 2008, pp. 3163–3168 (2008)Google Scholar
  67. 67.
    Waibel, M., Beetz, M., Civera, J., D’Andrea, R., Elfring, J., Galvez-Lopez, D., Haussermann, K., Janssen, R., Montiel, J., Perzylo, A., Schiessle, B., Tenorth, M., Zweigle, O., van de Molengraft, R.: RoboEarth. IEEE Robot. Autom. Mag. 18(2), 69–82 (2011)Google Scholar
  68. 68.
    Wang, L., Liu, M., Meng, M.Q.H.: Towards cloud robotic system: a case study of online co-localization for fair resource competence. In: IEEE International Conference on Robotics and Biomimetics (ROBIO 2012), pp. 2132–2137, December 2012Google Scholar
  69. 69.
    Wang, L., Liu, M., Meng, M.Q.H.: Real-time multi-sensor data retrieval for cloud robotic systems. IEEE Trans. Autom. Sci. Eng. 12(2) (2015)Google Scholar
  70. 70.
    Wang, L., Liu, M., Meng, M.Q.H., Siegwart, R.: Towards real-time multi-sensor information retrieval in cloud robotic system. In: 2012 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 21–26 (September 2012)Google Scholar
  71. 71.
    Wang, L., Liu, M., Meng, M.H.: An auction-based resource allocation strategy for joint-surveillance using networked multi-robot systems. In: 2013 IEEE International Conference on Information and Automation (ICIA), pp. 424–429, (August 2013)Google Scholar
  72. 72.
    Wang, L., Liu, M., Meng, M.H.: Hierarchical auction-based mechanism for real-time resource retrieval in cloud mobile robotic system. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), June 2014Google Scholar
  73. 73.
    Wang, L., Meng, M.H.: A game theoretical bandwidth allocation mechanism for cloud robotics. In: 2012 10th World Congress on Intelligent Control and Automation (WCICA), pp. 3828–3833, July 2012Google Scholar
  74. 74.
    Zhang, Y., Parker, L.E.: Multi-robot task scheduling. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 2992–2998. IEEE (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Electrical and Electronic EngineeringNanyang Techonological UniversitySingaporeSingapore
  2. 2.Department of Mechanical and Biomedical EngineeringCity University of Hong KongHong KongHong Kong
  3. 3.Department of Electronic EngineeringThe Chinese University of Hong KongHong KongHong Kong

Personalised recommendations