Hierarchical Distributed Task Allocation for Multi-robot Exploration

In order to more effectively explore a large unknown area, multiple robots may be employed to work cooperatively. When properly done, the group allocates specific portions of the overall exploration task to different robots such that the entire environment is explored with minimal excess effort. In this work, we present a new hierarchical market-based approach to this allocation problem. Our approach builds on standard auction approaches to provide agents with a mechanism to independently form coalitions and to divide a coalition into smaller coalitions in response to the progress of their cooperative exploration process. These coalitions allow a subset of the team to move together efficiently, especially in constrained environments when there are few avenues for exploration. We also present implementation and simulated experiments which show how this natural hierarchy forms and can lead to more efficient exploration than using a greedy allocation technique or without the use of coalitions.