We present an approach to task scheduling in heterogeneous multi-robot systems. In our setting, the tasks to complete require diverse skills. We assume that each robot is multi-skilled, i.e., each robot offers a subset of the possible skills. This makes the formation of heterogeneous teams (\emph{coalitions}) a requirement for task completion. We present two centralized algorithms to schedule robots across tasks and to form suitable coalitions, assuming stochastic travel times across tasks. The coalitions are dynamic, in that the robots form and disband coalitions as the schedule is executed. The first algorithm we propose guarantees optimality, but its run-time is acceptable only for small problem instances. The second algorithm we propose can tackle large problems with short run-times, and is based on a heuristic approach that typically reaches 1x-2x of the optimal solution cost.

翻译：我们提出了一种面向异构多机器人系统的任务调度方法。在本场景中，待完成的任务需要多种不同技能。我们假设每个机器人具备多技能特性，即每个机器人能提供全部技能中的部分子集。这使得异构团队（即“联盟”）的组建成为任务完成的必要条件。我们提出了两种集中式算法，在考虑跨任务随机行程时间的前提下，实现机器人跨任务调度与合适联盟的构建。这些联盟具有动态性，即机器人会根据调度执行情况组建或解散联盟。我们提出的第一种算法能够保证最优性，但其运行时间仅适用于小规模问题实例。第二种算法基于启发式方法，能够以较短运行时间处理大规模问题，其求解成本通常为最优解的1倍至2倍。