  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.
