Autonomous Robots

, Volume 38, Issue 3, pp 261–282 | Cite as

Model-predictive asset guarding by team of autonomous surface vehicles in environment with civilian boats

  • Eric Raboin
  • Petr Švec
  • Dana S. Nau
  • Satyandra K. Gupta
Article

Abstract

In this paper, we present a contract-based, decentralized planning approach for a team of autonomous unmanned surface vehicles (USV) to patrol and guard an asset in an environment with hostile boats and civilian traffic. The USVs in the team have to cooperatively deal with the uncertainty about which boats pose an actual threat and distribute themselves around the asset to optimize their guarding opportunities. The developed planner incorporates a contract-based algorithm for allocating tasks to the USVs through forward simulating the mission and assigning estimated utilities to candidate task allocation plans. The task allocation process uses a form of marginal cost-based contracting that allows decentralized, cooperative task negotiation among neighboring agents. The task allocation plans are realized through a corresponding set of low-level behaviors. In this paper, we demonstrate the planner using two mission scenarios. However, the planner is general enough to be used for a variety of scenarios with mission-specific tasks and behaviors. We provide detailed analysis of simulation results and discuss the impact of communication interruptions, unreliable sensor data, and simulation inaccuracies on the performance of the planner.

Keywords

Decentralized planning Task allocation Unmanned vehicles Unmanned surface vehicles 

Supplementary material

Supplementary material 1 (mp4 138864 KB)

References

  1. Bertaska, I. R., Alvarez, J., Sinisterra, A. J., von Ellenrieder, K., Dhanak, M., Shah, B. C., et al. (2013). Experimental evaluation of approach behavior for autonomous surface vehicles. In 6th Annual Dynamic Systems and Control Conference (DSCC ’13) Stanford University, Palo Alto. October 21–23.Google Scholar
  2. Bošanský, B., Lisý, V., Jakob, M., & Pěchouček, M. (2011). Computing time-dependent policies for patrolling games with mobile targets. In 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS’11).Google Scholar
  3. Corfield, S.J., & Young, J.M., (2006). Unmanned surface vehicles-game changing technology for naval operations. In Advances in unmanned arine vehicles (pp. 311–328). London: Institution of Engineering and Technology.Google Scholar
  4. Dias, M.B. (2004). Traderbots: A new paradigm for robust and efficient multirobot coordination in dynamic environments. PhD thesis, Carnegie Mellon University.Google Scholar
  5. Dias, M. B., Zlot, R., Kalra, N., & Stentz, A. (2006). Market-based multirobot coordination: A survey and analysis. Proceedings of the IEEE, 94(7), 1257–1270.CrossRefGoogle Scholar
  6. Fang, F., Jiang, A.X., & Tambe, M. (2013). Designing optimal patrol strategy for protecting moving targets with multiple mobile resources. In International Workshop on Optimisation in Multi-Agent Systems (OPTMAS).Google Scholar
  7. Gerkey, B. P., & Matarić, M. J. (2002). Sold!: Auction methods for multirobot coordination. IEEE Transactions on Robotics and Automation, 18(5), 758–768.CrossRefGoogle Scholar
  8. Gerkey, B. P., & Matarić, M. J. (2004). A formal analysis and taxonomy of task allocation in multi-robot systems. The International Journal of Robotics Research, 23(9), 939–954.CrossRefGoogle Scholar
  9. Holland, J. H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. Cambridge, MA, USA: MIT Press.Google Scholar
  10. Jakob, M., Vaněk, O., Hrstka, O., & Pěchouček, M. (2012). Agents vs. pirates: multi-agent simulation and optimization to fight maritime piracy. In 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS’12) (pp. 37–44). International Foundation for Autonomous Agents and Multiagent Systems.Google Scholar
  11. Kalra, N., Ferguson, D., & Stentz, A. (2005). Hoplites: A market-based framework for planned tight coordination in multirobot teams. In IEEE International Conference on Robotics and Automation (ICRA’05) (pp. 1170–1177). IEEE.Google Scholar
  12. Mosteo, A. R., & Montano, L. (2010). A survey of multi-robot task allocation. Instituto de Investigación en Ingeniería de Aragón.Google Scholar
  13. Parker, L. E. (2008). Multiple mobile robot systems. In Springer handbook of robotics (pp. 921–941). Berlin: Springer.Google Scholar
  14. Portugal, D., & Rocha, R. (2011). A survey on multi-robot patrolling algorithms. In Technological Innovation for Sustainability (pp. 139–146).Google Scholar
  15. Portugal, D., & Rocha, R.P. (2011). On the performance and scalability of multi-robot patrolling algorithms. In IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) (pp. 50–55). IEEE.Google Scholar
  16. Raboin, E., Švec, P., Nau, D. S., & Gupta, S. K. (2013). Model-predictive target defense by team of unmanned surface vehicles operating in uncertain environments. In IEEE International Conference on Robotics and Automation (ICRA’13).Google Scholar
  17. Sandholm, T. (1998). Contract types for satisficing task allocation. In Proceedings of the AAAI spring symposium: Satisficing models (pp. 23–25).Google Scholar
  18. Shieh, E.A., An, B., Yang, R., Tambe, M., Baldwin, C., DiRenzo, J., Maule, B., & Meyer, G. (2012). Protect: An application of computational game theory for the security of the ports of the united states. In AAAI Conference on Artificial Intelligence.Google Scholar
  19. Shoham, Y., & Leyton-Brown, K. (2010). Multiagent systems: Algorithmic, game-theoretic, and logical foundations. Cambridge: Cambridge University Press.Google Scholar
  20. Simetti, E., Turetta, A., Casalino, G., Storti, E., & Cresta, M. (2010). Protecting assets within a civilian harbour through the use of a team of USVs: Interception of possible menaces. OCEANS.Google Scholar
  21. Simmons, R., Apfelbaum, D., Fox, D., Goldman, R.P., Haigh, K.Z., Musliner, D.J., Pelican, M., & Thrun, S. (2000). Coordinated deployment of multiple, heterogeneous robots. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (volume 3, pp. 2254–2260). IEEE.Google Scholar
  22. Smith, R. G. (1980). The contract net protocol: high-level communication and control in a distributed problem solver. IEEE Transactions on Computers, 100(12), 1104–1113.CrossRefGoogle Scholar
  23. Švec, P., & Gupta, S. K. (2012). Automated synthesis of action selection policies for unmanned vehicles operating in adverse environments. Autonomous Robots, 32(2), 149–164.CrossRefGoogle Scholar
  24. Švec, P., Schwartz, M., Thakur, A., & Gupta, S. K. (2011). Trajectory planning with look-ahead for unmanned sea surface vehicles to handle environmental disturbances. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’11).Google Scholar
  25. Švec, P., Shah, B. C., Bertaska, I. R., Alvarez, J., Sinisterra, A. J., von Ellenrieder, K., et al. (2013). Dynamics-aware target following for an autonomous surface vehicle operating under COLREGs in civilian traffic. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’13) Tokyo. November 3–8.Google Scholar
  26. Švec, P., Thakur, A., & Gupta. S. K. (2012). USV trajectory planning for time varying motion goal in an environment with obstacles. In ASME 2012 International Design Engineering Technical Conferences (IDETC) & Computers and Information in Engineering Conference (CIE).Google Scholar
  27. Švec, P., Thakur, A., Shah, B. C., & Gupta, S. K. (2013). Target following with motion prediction for unmanned surface vehicle operating in cluttered environments. Autonomous Robots, 2013. Retrived for publication. doi:10.1007/s10514-013-9370-z.
  28. Tang, F., & Parker, L. E. (2007). A complete methodology for generating multi-robot task solutions using asymtre-d and market-based task allocation. In IEEE International Conference on Robotics and Automation (ICRA’07) (pp. 3351–3358).Google Scholar
  29. Thakur, A., & Gupta, S. K. (2011). Real-time dynamics simulation of unmanned sea surface vehicle for virtual environments. Journal of Computing and Information Science in Engineering, 11(3), 031005.CrossRefGoogle Scholar
  30. Thakur, A., Švec, P., & Gupta, S. K. (2012). Gpu based generation of state transition models using simulations for unmanned surface vehicle trajectory planning. In Robotics and Autonomous Systems.Google Scholar
  31. Vanek, O., Bosansky, B., Jakob, M., Lisy, V., & Pechoucek. M. (2012). Extending security games to defenders with constrained mobility. In Proceedings of AAAI Spring Symposium GTSSH.Google Scholar
  32. Zhang, Y., & Meng, Y. (2010). A decentralized multi-robot system for intruder detection in security defense. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’10) (pp. 5563–5568). IEEE.Google Scholar
  33. Zlot, R., & Stentz, A. (2006). Market-based multirobot coordination for complex tasks. The International Journal of Robotics Research, 25(1), 73–101.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Eric Raboin
    • 1
  • Petr Švec
    • 2
  • Dana S. Nau
    • 3
  • Satyandra K. Gupta
    • 4
  1. 1.Department of Computer ScienceUniversity of MarylandCollege ParkUSA
  2. 2.Simulation Based System Design Laboratory, Maryland Robotics Center, Department of Mechanical EngineeringUniversity of MarylandCollege ParkUSA
  3. 3.Department of Computer Science and Institute for Systems ResearchUniversity of MarylandCollege ParkUSA
  4. 4.Simulation Based System Design Laboratory, Maryland Robotics Center, Department of Mechanical Engineering and Institute for Systems ResearchUniversity of MarylandCollege ParkUSA

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