Multi-robot systems are uniquely well-suited to performing complex tasks such as patrolling and tracking, information gathering, and pick-up and delivery problems, offering significantly higher performance than single-robot systems. A fundamental building block in most multi-robot systems is task allocation: assigning robots to tasks (e.g., patrolling an area, or servicing a transportation request) as they appear based on the robots' states to maximize reward. In many practical situations, the allocation must account for heterogeneous capabilities (e.g., availability of appropriate sensors or actuators) to ensure the feasibility of execution, and to promote a higher reward, over a long time horizon. To this end, we present the FlowDec algorithm for efficient heterogeneous task-allocation achieving an approximation factor of at least 1/2 of the optimal reward. Our approach decomposes the heterogeneous problem into several homogeneous subproblems that can be solved efficiently using min-cost flow. Through simulation experiments, we show that our algorithm is faster by several orders of magnitude than a MILP approach.

中文翻译：

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通过流分解实现快速近乎最优的异构任务分配

多机器人系统非常适合执行复杂的任务，例如巡逻和跟踪、信息收集以及提货和交付问题，与单机器人系统相比，其性能要高得多。大多数多机器人系统中的一个基本构建块是任务分配：根据机器人的状态将机器人分配到任务（例如，巡逻区域，或为运输请求提供服务），以最大化奖励。在许多实际情况下，分配必须考虑异构能力（例如，适当传感器或执行器的可用性）以确保执行的可行性，并在很长一段时间内促进更高的奖励。为此，我们提出了 FlowDec 算法，用于高效的异构任务分配，实现至少 1/2 最佳奖励的近似因子。我们的方法将异构问题分解为几个可以使用最小成本流有效解决的同构子问题。通过模拟实验，我们表明我们的算法比 MILP 方法快几个数量级。