Building large-scale robot systems: Distributed role assignment in dynamic, uncertain domains

For robot teams in large-scale, real-world domains, an effective approach for allocating roles to team members (role allocation) is critical. Unfortunately, role allocation is extremely challenging in such real-world domains since robots face significant uncertainties in their own capabilities and they may be faced with dynamic and continuous changes in their capabilities. Previous work in multi-robotic and multiagent systems has provided algorithms for role allocation, but these algorithms often fail to explicitly represent and reason with uncertainty in a robot’s own capabilities, and often do not address dynamic continuous changes in such capabilities. This paper presents two key contributions to address these limitations in large-scale settings. First, it presents a novel role allocation and re-allocation algorithm that explicitly reasons with uncertainty and dynamic changes in robot’s own capabilities. Second, it presents the application of a proxy-based architecture (previously applied only in agent-human settings) in large-scale robotic domains — demonstrating the application of a reusable infrastructure for multirobotic domains. Moreover this paper presents an experimental study based on simulations performed in order to evaluate the algorithm. These simulations pave the way to large real-world practical multi-robotic system involving 100s of robots.

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