Reconfigurable Intelligent Surface for Internet of Robotic Things

TL;DR

This paper proposes a reconfigurable intelligent surface (RIS)-aided IoRT network to improve communication, sensing, and energy efficiency in multi-robot systems, using advanced optimization and deep reinforcement learning techniques.

Contribution

It introduces the integration of RIS with IoRT, optimizing multiple parameters to enhance performance in robotic communication, sensing, and energy management.

Findings

Improved communication quality and sensing accuracy.

Enhanced energy efficiency and reduced computation error.

Effective solutions demonstrated through numerical simulations.

Abstract

With the rapid development of artificial intelligence, robotics, and Internet of Things, multi-robot systems are progressively acquiring human-like environmental perception and understanding capabilities, empowering them to complete complex tasks through autonomous decision-making and interaction. However, the Internet of Robotic Things (IoRT) faces significant challenges in terms of spectrum resources, sensing accuracy, communication latency, and energy supply. To address these issues, a reconfigurable intelligent surface (RIS)-aided IoRT network is proposed to enhance the overall performance of robotic communication, sensing, computation, and energy harvesting. In the case studies, by jointly optimizing parameters such as transceiver beamforming, robot trajectories, and RIS coefficients, solutions based on multi-agent deep reinforcement learning and multi-objective optimization are proposed to solve problems such as beamforming design, path planning, target sensing, and data aggregation. Numerical results are provided to demonstrate the effectiveness of proposed solutions in improve communication quality, sensing accuracy, computation error, and energy efficiency of RIS-aided IoRT networks.