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MB Dias, R Zlot, N Kalra, A Stentz (2006)
Market-based multirobot coordination: A survey and analysisProceedings of the IEEE, 94
P Švec, SK Gupta (2012)
Automated synthesis of action selection policies for unmanned vehicles operating in adverse environmentsAutonomous Robots, 32
JH Holland (1992)
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
R Zlot, A Stentz (2006)
Market-based multirobot coordination for complex tasksThe International Journal of Robotics Research, 25
BP Gerkey, MJ Matarić (2002)
Sold!: Auction methods for multirobot coordinationIEEE Transactions on Robotics and Automation, 18
RG Smith (1980)
The contract net protocol: high-level communication and control in a distributed problem solverIEEE Transactions on Computers, 100
Y Shoham, K Leyton-Brown (2010)
Multiagent systems: Algorithmic, game-theoretic, and logical foundations
BP Gerkey, MJ Matarić (2004)
A formal analysis and taxonomy of task allocation in multi-robot systemsThe International Journal of Robotics Research, 23
A Thakur, SK Gupta (2011)
Real-time dynamics simulation of unmanned sea surface vehicle for virtual environmentsJournal of Computing and Information Science in Engineering, 11
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.
Autonomous Robots – Springer Journals
Published: Aug 6, 2014
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