Allocating Spatially Distributed Tasks in Large, Dynamic Robot Teams

Abstract

For an interesting class of emerging applications, a large robot team will need to distributedly allocate many more tasks than there are robots, with dynamically appearing tasks and a limited ability to communicate. The LA-DCOP algorithm can conceptually handle both large-scale problems and multiple tasks per robot, but has key limitations when allocating spatially distributed tasks. In this paper, we extend LA-DCOP with several alternative acceptance rules for robots to determine whether to take on an additional task, given the interaction with the tasks it has already committed to. We show that these acceptance rules dramatically outperform a naive LA-DCOP implementation. In addition, we developed a technique that lets the robots use completely local knowledge to adjust their task acceptance criteria to get the best possible performance at a given communication bandwidth level.