Cloud Robotics is helping to create a new generation of robots that leverage the nearly unlimited resources of large data centers (i.e., the cloud), overcoming the limitations imposed by on-board resources. Different processing power, capabilities, resource sizes, energy consumption, and so forth, make scheduling and task allocation critical components. The basic idea of task allocation and scheduling is to optimize performance by minimizing completion time, energy consumption, delays between two consecutive tasks, along with others, and maximizing resource utilization, number of completed tasks in a given time interval, and suchlike. In the past, several works have addressed various aspects of task allocation and scheduling. In this paper, we provide a comprehensive overview of task allocation and scheduling strategies and related metrics suitable for robotic network cloud systems. We discuss the issues related to allocation and scheduling methods and the limitations that need to be overcome. The literature review is organized according to three different viewpoints: Architectures and Applications, Methods and Parameters. In addition, the limitations of each method are highlighted for future research.

翻译：云机器人正助力构建新一代机器人，这些机器人利用大型数据中心（即云端）近乎无限的资源，突破了机载资源的限制。不同的处理能力、功能、资源规模、能耗等因素，使得调度与任务分配成为关键环节。任务分配与调度的基本思想是通过最小化完成时间、能耗、连续任务间的延迟等指标来优化性能，同时最大化资源利用率、给定时间间隔内完成的任务数量等。过去已有许多研究探讨了任务分配与调度的各个方面。本文全面综述了适用于机器人网络云系统的任务分配与调度策略及相关指标，讨论了分配与调度方法中存在的问题及需要克服的局限性。文献综述按照三个不同视角组织：架构与应用、方法与参数。此外，还突出了每种方法的局限性，以供未来研究参考。