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.

中文翻译：

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带时间约束的分布式任务调度，用于优化多机器人系统中的总任务分配

本文考虑了在严格的时间约束下最大化分布式机器人系统中的任务分配数量的问题，其中还需要考虑其他优化目标。它以现有的分布式任务分配算法为基础，并通过一种新颖的方法对其进行了扩展，以最大化任务分配的数量。基本思想是，如果将任务分配给另一个机器人会为未分配的任务创建可行的时隙，则分配给机器人的任务成本很高。可能需要在联网机器人之间进行多次重新分配以创建可行的时隙，并且可以根据性能要求来调整此重新分配数量的上限。通过与任务截止日期和燃料限制的模拟救援场景，与现有方法，基于共识的捆绑算法和性能影响（PI）算法相比，该方法的性能得到了证明。从现有的（PI生成的）解决方案开始，使用所提出的方法，结果表明任务分配最多增加了20％。