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This paper deals with large-scale decentralised task allocation problems formultiple heterogeneous robots with monotone submodular objective functions. Oneof the significant challenges with the large-scale decentralised taskallocation problem is the NP-hardness for computation and communication. Thispaper proposes a decentralised Decreasing Threshold Task Allocation (DTTA)algorithm that enables parallel allocation by leveraging a decreasing thresholdto handle the NP-hardness. Then DTTA is upgraded to a more practical versionLazy Decreasing Threshold Task Allocation (LDTTA) by combining a variant ofLazy strategy. DTTA and LDTTA can release both computational and communicatingburden for multiple robots in a decentralised network while providing anoptimality bound of solution quality. To examine the performance of theproposed algorithms, this paper models a multi-target surveillance scenario andconducts Monte-Carlo simulations. Simulation results reveal that the proposedalgorithms achieve similar function values but consume much less running timeand consensus steps compared with benchmark decentralised task allocationalgorithms.

Threshold Greedy Based Task Allocation for Multiple Robot Operations