Distributed task rescheduling with time constraints for the optimization of total task allocations in a multirobot system

This paper considers the problem of maximising the number of task allocations in a distributed multi-robot system under strict time constraints, where other optimisation objectives need also be considered. This study builds upon existing distributed task allocation algorithms, extending them with a novel method for maximising 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 (CBBA) and the Performance Impact algorithm (PI). Starting from existing (PI-generated) solutions, results show an up to 20% increase in task allocations using the proposed method.