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Autonomous Robots
Erschienen in: Autonomous Robots 3/2015

01.03.2015

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

verfasst von: Eric Raboin, Petr Švec, Dana S. Nau, Satyandra K. Gupta

Erschienen in: Autonomous Robots | Ausgabe 3/2015

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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.
Vorheriger Artikel Feature-based map merging with dynamic consensus on information increments
Nächster Artikel Towards docking for small scale underwater robots

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Literatur
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.
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).
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.
Dias, M.B. (2004). Traderbots: A new paradigm for robust and efficient multirobot coordination in dynamic environments. PhD thesis, Carnegie Mellon University.
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.CrossRef
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).
Gerkey, B. P., & Matarić, M. J. (2002). Sold!: Auction methods for multirobot coordination. IEEE Transactions on Robotics and Automation, 18(5), 758–768.CrossRef
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.CrossRef
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.
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.
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.
Mosteo, A. R., & Montano, L. (2010). A survey of multi-robot task allocation. Instituto de Investigación en Ingeniería de Aragón.
Parker, L. E. (2008). Multiple mobile robot systems. In Springer handbook of robotics (pp. 921–941). Berlin: Springer.
Portugal, D., & Rocha, R. (2011). A survey on multi-robot patrolling algorithms. In Technological Innovation for Sustainability (pp. 139–146).
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.
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).
Sandholm, T. (1998). Contract types for satisficing task allocation. In Proceedings of the AAAI spring symposium: Satisficing models (pp. 23–25).
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.
Shoham, Y., & Leyton-Brown, K. (2010). Multiagent systems: Algorithmic, game-theoretic, and logical foundations. Cambridge: Cambridge University Press.
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.
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.
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.CrossRef
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.CrossRef
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).
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.
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).
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.
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).
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.CrossRef
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.
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.
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.
Zlot, R., & Stentz, A. (2006). Market-based multirobot coordination for complex tasks. The International Journal of Robotics Research, 25(1), 73–101.CrossRef
Metadaten
Titel
Model-predictive asset guarding by team of autonomous surface vehicles in environment with civilian boats
verfasst von
Eric Raboin
Petr Švec
Dana S. Nau
Satyandra K. Gupta
Publikationsdatum
01.03.2015
Verlag
Springer US
Erschienen in
Autonomous Robots / Ausgabe 3/2015
Print ISSN: 0929-5593
Elektronische ISSN: 1573-7527
DOI
https://doi.org/10.1007/s10514-014-9409-9

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