Abstract

The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies or endorsements, either expressed or implied, of Carnegie Mellon University. The coordination of a large group of robots to solve a specified task is a difficult problem. Centralized approaches can be computationally intractable, brittle, and unresponsive to change. Distributed approaches are not as prone to these problems, but they can be highly sub-optimal. This work introduces a novel approach for coordinating robots based on the free market system. Market economies are a proven way to organize a large number of individuals into a productive group. The free market approach defines revenue and cost functions across the possible plans for executing a specified task. The task is accomplished by dividing it into sub-tasks and allowing the robots to bid and negotiate to carry out these sub-tasks. Cooperation and competition emerge as the robots execute the task while trying to maximize their personal profits. The result promises to be a highly robust multirobot team that can efficiently exploit resources and opportunistically deal with uncertainties in a dynamic environment. A detailed example of how this model could be applied to a foraging task is presented and the different characteristics of the approach are highlighted in the context of the example. The ability to scale this approach to

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