  Indoor multi-robot communications face two key challenges: one is the severe
signal strength degradation caused by blockages (e.g., walls) and the other is
the dynamic environment caused by robot mobility. To address these issues, we
consider the reconfigurable intelligent surface (RIS) to overcome the signal
blockage and assist the trajectory design among multiple robots. Meanwhile, the
non-orthogonal multiple access (NOMA) is adopted to cope with the scarcity of
spectrum and enhance the connectivity of robots. Considering the limited
battery capacity of robots, we aim to maximize the energy efficiency by jointly
optimizing the transmit power of the access point (AP), the phase shifts of the
RIS, and the trajectory of robots. A novel federated deep reinforcement
learning (F-DRL) approach is developed to solve this challenging problem with
one dynamic long-term objective. Through each robot planning its path and
downlink power, the AP only needs to determine the phase shifts of the RIS,
which can significantly save the computation overhead due to the reduced
training dimension. Simulation results reveal the following findings: I) the
proposed F-DRL can reduce at least 86% convergence time compared to the
centralized DRL; II) the designed algorithm can adapt to the increasing number
of robots; III) compared to traditional OMA-based benchmarks, NOMA-enhanced
schemes can achieve higher energy efficiency.
