A Survey on Task Allocation and Scheduling in Robotic Network Systems

Abstract

Robotic networks are increasingly relied upon to perform complex tasks that require efficient scheduling and task allocation to optimize processing power, resource management, and energy use. The primary goal in these systems is to enhance performance by minimizing completion time, energy consumption, and delays, while maximizing resource utilization and task throughput. Numerous studies have examined different aspects of task allocation and scheduling, from static approaches to dynamic models that adapt to real-time conditions. This article presents a comprehensive survey of the methods and strategies used in robotic network systems, considering not only traditional approaches but also the role of emerging technologies, such as cloud, fog, and edge computing. We categorize the literature from three perspectives: 1) architectures and applications; 2) methods; and 3) parameters. Furthermore, we analyze the limitations of each approach and propose directions for future research, with a particular focus on scalability, real-world applicability, and the integration of these technologies in dynamic environments.