1 research outputs found
Threshold Greedy Based Task Allocation for Multiple Robot Operations
This paper deals with large-scale decentralised task allocation problems for
multiple heterogeneous robots with monotone submodular objective functions. One
of the significant challenges with the large-scale decentralised task
allocation problem is the NP-hardness for computation and communication. This
paper proposes a decentralised Decreasing Threshold Task Allocation (DTTA)
algorithm that enables parallel allocation by leveraging a decreasing threshold
to handle the NP-hardness. Then DTTA is upgraded to a more practical version
Lazy Decreasing Threshold Task Allocation (LDTTA) by combining a variant of
Lazy strategy. DTTA and LDTTA can release both computational and communicating
burden for multiple robots in a decentralised network while providing an
optimality bound of solution quality. To examine the performance of the
proposed algorithms, this paper models a multi-target surveillance scenario and
conducts Monte-Carlo simulations. Simulation results reveal that the proposed
algorithms achieve similar function values but consume much less running time
and consensus steps compared with benchmark decentralised task allocation
algorithms