Auction-based dynamic task allocation for foraging with a cooperative robot team

Many application domains require search and retrieval, which is also known in the robotic domain as foraging. An example domain is search and rescue where a disaster area needs to be explored and transportation of survivors to a safe area needs to be arranged. Performing these tasks by more than one robot increases performance if tasks are allocated and executed efficiently. In this paper, we study the Multi-Robot Task Allocation (MRTA) problem in the foraging domain. We assume that a team of robots is cooperatively searching for targets of interest in an environment which need to be retrieved and brought back to a home base. We look at a more general foraging problem than is typically studied where coordination also requires to take temporal constraints into account. As usual, robots have no prior knowledge about the location of targets, but in addition need to deliver targets to the home base in a specific order. This significantly increases the complexity of a foraging problem. We use a graph-based model to analyse the problem and the dynamics of allocating exploration and retrieval tasks. Our main contribution is an extension of auction-based approaches to deal with dynamic foraging task allocation where not all tasks are initially known. We use the Blocks World for Teams (BW4T) simulator to evaluate the proposed approach.