论文标题
机器人网络云系统的动态任务分配
Dynamic Task Allocation for Robotic Network Cloud Systems
论文作者
论文摘要
每个机器人网络云系统都可以看作是带有节点作为硬件的图形，并具有独立的计算处理功能和边缘作为节点之间的数据传输。在将任务分配给节点时，我们可能会更改与节点相对应的几个值，例如与其他节点的距离，完成其所有任务的时间，节点的能量水平，消耗的能量，同时执行其所有任务，几何位置，与其他节点的通信等等。这些值可以看作是节点当前状态的指纹，可以评估为超空间的子空间。我们提出了一个理论模型，描述了将任务分配给节点的方式将改变超空间的子空间，因此，我们展示了如何获得最佳任务分配。我们将节点之间的通信不稳定性和节点的能力作为超空间的子空间。我们将任务调度转换为节点，以查找超空间的最大体积。
Every robotic network cloud system can be seen as a graph with nodes as hardware with independent computational processing powers and edges as data transmissions between nodes. When assigning a task to a node we may change several values corresponding to the node such as distance to other nodes, the time to complete all of its tasks, the energy level of the node, energy consumed while performing all of its tasks, geometrical position, communication with other nodes, and so on. These values can be seen as fingerprints for the current state of the node which can be evaluated as a subspace of a hyperspace. We proposed a theoretical model describing how assigning tasks to a node will change the subspace of the hyperspace, and from that, we show how to obtain the optimal task allocation. We described the communication instability between nodes and the capability of nodes as subspaces of a hyperspace. We translate task scheduling to nodes as finding the maximum volume of the hyperspace.
