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Reconfigurable Intelligent Surface for Internet of Robotic Things

Wanli Ni, Ruyu Luo, Xinran Zhang, Peng Wang, Wen Wang, Hui Tian

TL;DR

This paper introduces reconfigurable intelligent surfaces (RIS) to the Internet of Robotic Things (IoRT) to address spectrum, sensing, latency, and energy limitations in multi-robot systems. It presents three case studies that jointly optimize communication, sensing, computation, and wireless charging using federated deep reinforcement learning and multi-objective optimization, demonstrating improvements in energy efficiency, sensing accuracy, and data aggregation. The results show that RIS-enabled environments can significantly enhance robotic performance, trajectory planning, beamforming, and over-the-air computation under dynamic conditions. The work highlights open challenges such as URLLC, security, embodied intelligence, and the deployment of advanced services in RIS-aided IoRT networks, outlining a roadmap for future research and real-world impact.

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.

Reconfigurable Intelligent Surface for Internet of Robotic Things

TL;DR

This paper introduces reconfigurable intelligent surfaces (RIS) to the Internet of Robotic Things (IoRT) to address spectrum, sensing, latency, and energy limitations in multi-robot systems. It presents three case studies that jointly optimize communication, sensing, computation, and wireless charging using federated deep reinforcement learning and multi-objective optimization, demonstrating improvements in energy efficiency, sensing accuracy, and data aggregation. The results show that RIS-enabled environments can significantly enhance robotic performance, trajectory planning, beamforming, and over-the-air computation under dynamic conditions. The work highlights open challenges such as URLLC, security, embodied intelligence, and the deployment of advanced services in RIS-aided IoRT networks, outlining a roadmap for future research and real-world impact.

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.

Paper Structure

This paper contains 28 sections, 4 figures.

Table of Contents

  1. Introduction
  2. RIS-Aided IoRT Networks
  3. RIS-Aided Robotic Communication
  4. RIS-Aided Robotic Sensing
  5. RIS-Aided Robotic Computation
  6. RIS-Aided Robotic Wireless Charging
  7. Case Studies in RIS-Aided IoRT Networks
  8. Case 1: Joint Design of Communication and Trajectory
  9. Background
  10. System Model
  11. Proposed Solution
  12. Simulation Results
  13. Case 2: Joint Design of Communication and Sensing
  14. Background
  15. System Model
  16. ...and 13 more sections

Figures (4)

  • Figure 1: An illustration of RIS-aided robotic communication, sensing, computation, and wireless charging in IoRT networks.
  • Figure 2: System model, algorithm design, and simulation results of the joint optimization of communication and trajectory in RIS-aided IoRT networks.
  • Figure 3: An illustration of the joint optimization of robotic communication and sensing in IoRT networks.
  • Figure 4: An illustration for the joint optimization of communication, over-the-air computation, and wireless charging in RIS-aided IoRT networks.