One simplifying assumption made in distributed robot systems is that the robots are single-tasking: each robot operates on a single task at any time. While such a sanguine assumption is innocent to make in situations with sufficient resources so that the robots can operate independently, it becomes impractical when they must share their capabilities. In this paper, we consider multi-tasking robots with multi-robot tasks. Given a set of tasks, each achievable by a coalition of robots, our approach allows the coalitions to overlap and task synergies to be exploited by reasoning about the physical constraints that can be synergistically satisfied for achieving the tasks. The key contribution of this work is a general and flexible framework to achieve this ability for multi-robot systems in resource-constrained situations to extend their capabilities. The proposed approach is built on the information invariant theory, which specifies the interactions between information requirements. In our work, we map physical constraints to information requirements, thereby allowing task synergies to be identified via the information invariant framework. We show that our algorithm is sound and complete under a problem setting with multi-tasking robots. Simulation results show its effectiveness under resource-constrained situations and in handling challenging situations in a multi-UAV simulator.

中文翻译：

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在多机器人任务中实现多任务机器人

在分布式机器人系统中做出的一个简化假设是机器人是单任务的：每个机器人在任何时候都在执行一项任务。虽然在资源充足的情况下做出这种乐观的假设是无辜的，因此机器人可以独立运行，但当它们必须共享它们的能力时就变得不切实际了。在本文中，我们考虑具有多机器人任务的多任务机器人。给定一组任务，每个任务都可以由机器人联盟实现，我们的方法允许联盟重叠，并通过推理可以协同满足完成任务的物理约束来利用任务协同作用。这项工作的主要贡献是一个通用且灵活的框架，可以在资源受限的情况下实现多机器人系统扩展其能力的能力。所提出的方法建立在信息不变理论的基础上，该理论指定了信息需求之间的相互作用。在我们的工作中，我们将物理约束映射到信息需求，从而允许通过信息不变框架识别任务协同作用。我们表明我们的算法在多任务机器人的问题设置下是健全和完整的。仿真结果显示了其在资源受限情况下以及在多无人机模拟器中处理具有挑战性的情况时的有效性。我们表明我们的算法在多任务机器人的问题设置下是健全和完整的。仿真结果显示了其在资源受限情况下以及在多无人机模拟器中处理具有挑战性的情况时的有效性。我们表明我们的算法在多任务机器人的问题设置下是健全和完整的。仿真结果显示了其在资源受限情况下以及在多无人机模拟器中处理具有挑战性的情况时的有效性。