A Survey on Task Allocation and Scheduling in Robotic Network Systems

TL;DR

This survey reviews task allocation and scheduling strategies in robotic network cloud systems, highlighting methods, challenges, and future research directions to optimize performance and resource utilization.

Contribution

It provides a comprehensive overview of existing strategies, categorizes them by architecture and method, and discusses their limitations for future improvements.

Findings

Various strategies are used for task allocation and scheduling.

Limitations exist in current methods that hinder optimal performance.

Future research should address these limitations for better efficiency.

Abstract

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.