Dynamic Task Allocation for Robotic Network Cloud Systems

TL;DR

This paper presents a theoretical model for dynamic task allocation in robotic network cloud systems, using hyperspace subspaces to optimize task assignment based on node states and communication stability.

Contribution

It introduces a novel hyperspace-based model to analyze and optimize task allocation considering node states and communication factors.

Findings

Model describes how task assignment affects node state subspaces

Optimal task allocation corresponds to maximizing hyperspace volume

Addresses communication instability in task scheduling

Abstract

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.