  Computation load-sharing across a network of heterogeneous robots is a
promising approach to increase robots capabilities and efficiency as a team in
extreme environments. However, in such environments, communication links may be
intermittent and connections to the cloud or internet may be nonexistent. In
this paper we introduce a communication-aware, computation task scheduling
problem for multi-robot systems and propose an integer linear program (ILP)
that optimizes the allocation of computational tasks across a network of
heterogeneous robots, accounting for the networked robots' computational
capabilities and for available (and possibly time-varying) communication links.
We consider scheduling of a set of inter-dependent required and optional tasks
modeled by a dependency graph. We present a consensus-backed scheduling
architecture for shared-world, distributed systems. We validate the ILP
formulation and the distributed implementation in different computation
platforms and in simulated scenarios with a bias towards lunar or planetary
exploration scenarios. Our results show that the proposed implementation can
optimize schedules to allow a threefold increase the amount of rewarding tasks
performed (e.g., science measurements) compared to an analogous system with no
computational load-sharing.
