Certain tasks that cannot be completed with available robotic resources are candidates for the use of cloud robotics, such that individual robot limitations are overcome. Previous studies have mainly focused on minimizing the cost of the robots in retrieving resources by knowing the resource allocation in advance. We develop a method for a robotic network cloud system that includes robots, fog and cloud nodes, to determine where each algorithm should be allocated so that the system achieves optimal performance, regardless of which robot initiates the request. We can find the minimum required memory for the robots and the optimal way to allocate the algorithms with the shortest time to complete each task. We experimentally compare our method with a state-of-the-art method, using real-world data, showing the improvements that can be obtained.

翻译：以可用的机器人资源无法完成的某些任务是使用云型机器人的候选任务,这样就克服了单个机器人的限制。 先前的研究主要侧重于通过事先了解资源分配情况来尽量减少机器人在获取资源方面的成本。 我们开发了机器人网络云系统的方法,其中包括机器人、雾和云节点,以确定每个算法应分配到何处,以使系统达到最佳性能,不管哪个机器人提出请求。 我们可以找到机器人所需的最起码的内存,以及用最短的时间来分配算法以完成每项任务的最佳方式。 我们用现实世界的数据,实验地比较我们的方法和最先进的方法,展示可以取得的改进。