  In this paper, we consider the problem of allocating human operator
assistance in a system with multiple autonomous robots. Each robot is required
to complete independent missions, each defined as a sequence of tasks. While
executing a task, a robot can either operate autonomously or be teleoperated by
the human operator to complete the task at a faster rate. We show that the
problem of creating a teleoperation schedule that minimizes makespan of the
system is NP-Hard. We formulate our problem as a Mixed Integer Linear Program,
which can be used to optimally solve small to moderate sized problem instances.
We also develop an anytime algorithm that makes use of the problem structure to
provide a fast and high-quality solution of the operator scheduling problem,
even for larger problem instances. Our key insight is to identify blocking
tasks in greedily-created schedules and iteratively remove those blocks to
improve the quality of the solution. Through numerical simulations, we
demonstrate the benefits of the proposed algorithm as an efficient and scalable
approach that outperforms other greedy methods.
