用智能表面提升机器人物联网的通信感知能力
Reconfigurable Intelligent Surface for Internet of Robotic Things
- 通过智能表面联合优化机器人轨迹与波束成形
- 显著提升通信质量、感知精度与能量效率
- 适合研究智能城市与多机器人协同系统者
随着人工智能、机器人技术和物联网的快速发展，多机器人系统正逐步具备类人环境感知与理解能力，能够通过自主决策和交互完成复杂任务。然而，机器人物联网（IoRT）在频谱资源、感知精度、通信延迟和能源供应方面面临严峻挑战。为此，本文提出一种基于可重构智能表面（RIS）的IoRT网络，以增强机器人的通信、感知、计算与能量采集性能。通过联合优化发射接收波束成形、机器人轨迹与RIS系数，采用多智能体深度强化学习与多目标优化方法，解决波束设计、路径规划、目标感知与数据聚合等问题。数值结果表明，所提方案能有效提升RIS辅助IoRT网络的通信质量、感知精度、计算误差降低率及能量效率。
原文摘要 · Abstract (English)
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.
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