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Distributed Multi-Robot Task Allocation through Vacancy Chains

Existing multi-robot task allocation algorithms (MRTA) generally do not consider the effects of interaction, such as interference, but instead assume that tasks are independent. That assumption, however, is often violated in groups of cooperative mobile robots, where interaction effects can have a critical impact on performance. Modeling the effects of interaction, or group dynamics, is problematic due to their complexity and volatility, which also makes it difficult to hand-code optimal solutions to MRTA problems. We present a distributed MRTA algorithm that is sensitive to interaction dynamics. The algorithm uses distributed reinforcement learning to make interference-sensitive estimates of task utilities and relies on stigmergy to let optimal allocations emerge. The algorithm is inspired by vacancy chains, a resource distribution process common in human and animal societies. We present a formal model of task allocation through vacancy chains that defines optimal allocations in MRTA problems in terms of interference-sensitive measures of individual robot contributions. We validate our model by demonstrating, in simulation, how the predicted allocations are produced by our algorithm. As the robots continuously update their individual utility estimates, the vacancy chain algorithm has the additional property of adapting automatically to changes in the environment, e.g., robot breakdowns or changes in task values. We study the problem of cooperative transportation and demonstrate that our algorithm improves on the performance of hand-coded solutions. Finally, as the vacancy chain algorithm uses no communication or unique roles and is more robust and scalable than alternative algorithms with significant communication overheads.