Achieving Multitasking Robots in Multi-Robot Tasks

One simplifying assumption made in the existing and well-performing multi-robot systems is that the robots are single-tasking: each robot operates on a single task at any time. While this assumption is innocent to make in situations with sufficient resources such that robots can work independently, it becomes a restriction when they must share capabilities. In this paper, we consider multitasking robots with multi-robot tasks. Given a set of tasks, each achievable by a coalition of robots, our approach allows the coalitions to overlap by exploiting task synergies based on the physical constraints required to maintain these coalitions. The key contribution is a general and flexible framework that extends the current multi-robot systems to enable multitasking. The proposed approach is inspired by the information invariant theory, which orients around the equivalence of different information requirements. We map physical constraints to information requirements in our work, thereby allowing task synergies to be identified by reasoning about the relationships between such requirements. We show that our algorithm is sound and complete. Simulation results show its effectiveness under resource-constrained situations and in handling challenging scenarios in a realistic UAV simulator.