Optimal Algorithm Allocation for Robotic Network Cloud Systems

TL;DR

This paper presents an optimal algorithm allocation method for robotic network cloud systems, enhancing performance by efficiently distributing tasks among robots, fog, and cloud nodes, and demonstrating improvements over existing methods.

Contribution

It introduces a novel approach to allocate algorithms in robotic cloud systems for optimal performance, considering system-wide efficiency and real-world data validation.

Findings

Achieves minimal robot memory requirements.

Provides shortest task completion times.

Outperforms state-of-the-art allocation methods.

Abstract

A robotic network is a system with multiple robots connected by a communication network. Certain tasks that cannot be accomplished with available robotic resources are candidates for the use of cloud robotics, which overcomes the limitations of the robot network by adding to the network, either local or remote servers or cloud infrastructure, to aid in computational demanding tasks or storage. 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.