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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 economic approach for coordinating robots based on the free market system. 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. Initial simulation results indicate the approach is successful at producing effective global plans for a team of several robots performing an interior sensing task.
Dias et al. (2018) studied this question.