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.

提出了一个利用可重构智能表面支持的多机器人网络，目标是通过联合优化轨迹和机器人的 NOMA 解码顺序，RIS 的相移系数以及 AP 的功率分配，最大化整个轨迹的总速率。通过整合深度 Q 网络算法和长短时记忆自回归移动平均模型，预测每个机器人的初始和结束位置，同时确定机器人的轨迹，数值结果表明：LSTM-ARIMA 模型提供高精度的预测模型；所提出的 D^3QN 算法可以实现快速平均收敛；RIS-NOMA 网络相对于 RIS 支持的正交网络具有更高的网络性能。