TraderBots: A Market-Based Approach for Resource, Role, and Task Allocation in Multirobot Coordination

Abstract

The problem of efficient multirobot coordination has risen to the forefront of robotics research in recent years. Interest in this problem is motivated by the wide range of application domains demanding multirobot solutions. In general, multirobot coordination strategies assume either a centralized approach, where a single robot/agent plans for the group, or a distributed approach, where each robot is responsible for its own planning. Inherent to many centralized approaches are several difficulties. The key advantage of centralized approaches is that they can produce globally optimal plans. While most distributed approaches can overcome the obstacles inherent to centralized approaches, they can only produce suboptimal plans. This work presents the philosophy and traces the development of ?raderBots? a market-based architecture that is inherently distributed, but also capable of opportunistically forming centralized sub-groups to improve efficiency. Robots are self-interested with the primary goal of maximizing individual profits. The revenue/cost models and rules of engagement are designed so that maximizing individual profit has the benevolent effect of moving the team toward the globally optimal solution.