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.

翻译：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获得类人的环境感知与理解能力，使其能够通过自主决策与交互完成复杂任务。然而，机器人物联网在频谱资源、感知精度、通信时延和能量供给方面面临重大挑战。为解决这些问题，本文提出了一种基于可重构智能表面辅助的机器人物联网网络，以增强机器人通信、感知、计算与能量收集的整体性能。在案例研究中，通过联合优化收发器波束成形、机器人轨迹和RIS系数等参数，提出了基于多智能体深度强化学习和多目标优化的解决方案，以解决波束成形设计、路径规划、目标感知和数据聚合等问题。数值结果验证了所提方案在提升RIS辅助的IoRT网络的通信质量、感知精度、计算误差和能量效率方面的有效性。