Local and Nonlocal Human-to-Robot Task Allocation in Fiber-Wireless Multi-Robot Networks

Abstract

Integrated fiber-wireless (FiWi) multi-robot networks will play a pivotal role in ensuring QoS for several human-to-robot (H2R) applications due to their coverage and capacity advantages. For the successful deployment of H2R applications, efficient task allocation among robots is essential, which has emerged as an interesting research topic by taking into account a wide variety of task and robot types, task location, robot availability, capability, and failure during task execution. To render the task allocation process more efficient, we propose a task allocation scheme for FiWi-based multi-robot networks according to several key design parameters such as the availability, skill set, distance to task location, and remaining energy of robots. Furthermore, to reduce failures during task execution, we introduce a neighboring robot-assisted failure reporting mechanism. We develop an analytical model to evaluate the network performance in terms of throughput, task allocation delay, execution time, and residual energy. In addition, we analyze the end-to-end delay performance for both local and nonlocal task allocation in integrated FiWi multi-robot networks. Our results show that minimum execution time-based selection outperforms traditional minimum distance and priority-based selection in terms of total task execution time and average residual energy.