This paper considers the problem of maximizing the number of task allocations in a distributed multirobot system under strict time constraints, where other optimization objectives need also be considered. It builds upon existing distributed task allocation algorithms, extending them with a novel method for maximizing the number of task assignments. The fundamental idea is that a task assignment to a robot has a high cost if its reassignment to another robot creates a feasible time slot for unallocated tasks. Multiple reassignments among networked robots may be required to create a feasible time slot and an upper limit to this number of reassignments can be adjusted according to performance requirements. A simulated rescue scenario with task deadlines and fuel limits is used to demonstrate the performance of the proposed method compared with existing methods, the consensus-based bundle algorithm and the performance impact (PI) algorithm. Starting from existing (PI-generated) solutions, results show up to a 20% increase in task allocations using the proposed method.
CITATION STYLE
Turner, J., Meng, Q., Schaefer, G., Whitbrook, A., & Soltoggio, A. (2018). Distributed Task Rescheduling with Time Constraints for the Optimization of Total Task Allocations in a Multirobot System. IEEE Transactions on Cybernetics, 48(9), 2583–2597. https://doi.org/10.1109/TCYB.2017.2743164
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