A novel reconfigurable intelligent surface-aided multi-robot network is proposed, where multiple mobile robots are served by an access point (AP) through non-orthogonal multiple access (NOMA). The goal is to maximize the sum-rate of whole trajectories for the multi-robot system by jointly optimizing trajectories and NOMA decoding orders of robots, phase-shift coefficients of the RIS, and the power allocation of the AP, subject to predicted initial and final positions of robots and the quality of service (QoS) of each robot. To tackle this problem, an integrated machine learning (ML) scheme is proposed, which combines long short-term memory (LSTM)-autoregressive integrated moving average (ARIMA) model and dueling double deep Q-network (D$^{3}$QN) algorithm. For initial and final position prediction for robots, the LSTM-ARIMA is able to overcome the problem of gradient vanishment of non-stationary and non-linear sequences of data. For jointly determining the phase shift matrix and robots' trajectories, D$^{3}$QN is invoked for solving the problem of action value overestimation. Based on the proposed scheme, each robot holds an optimal trajectory based on the maximum sum-rate of a whole trajectory, which reveals that robots pursue long-term benefits for whole trajectory design. Numerical results demonstrated that: 1) LSTM-ARIMA model provides high accuracy predicting model; 2) The proposed D$^{3}$QN algorithm can achieve fast average convergence; and 3) RIS-NOMA networks have superior network performance compared to RIS-aided orthogonal counterparts.

翻译：提出了一种新颖的可重构智能表面辅助多机器人网络，其中多个移动机器人通过非正交多址接入（NOMA）由接入点（AP）提供服务。目标是在给定机器人预测初始和最终位置以及每个机器人服务质量（QoS）约束的条件下，通过联合优化机器人的轨迹和NOMA解码顺序、RIS的相移系数以及AP的功率分配，最大化多机器人系统整个轨迹的总和速率。为解决该问题，提出了一种集成机器学习（ML）方案，该方案结合了长短期记忆（LSTM）-自回归积分滑动平均（ARIMA）模型和双决斗深度Q网络（D$^{3}$QN）算法。对于机器人的初始和最终位置预测，LSTM-ARIMA能够克服非平稳和非线性数据序列的梯度消失问题。对于联合确定相移矩阵和机器人轨迹，引入D$^{3}$QN以解决动作价值过估计问题。基于所提方案，每个机器人在整个轨迹最大总和速率基础上获得最优轨迹，这表明机器人在整个轨迹设计中追求长期收益。数值结果表明：1) LSTM-ARIMA模型提供了高精度预测模型；2) 所提D$^{3}$QN算法能够实现快速平均收敛；3) RIS-NOMA网络相比RIS辅助的正交网络具有更优的网络性能。