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A novel intelligent reflecting surface (IRS)-aided multi-robot network isproposed, where multiple mobile wheeled robots are served by an access point(AP) through non-orthogonal multiple access (NOMA). The goal is to maximize thesum-rate of all robots by jointly optimizing trajectories and NOMA decodingorders of robots, reflecting coefficients of the IRS, and the power allocationof the AP, subject to the quality of service (QoS) of each robot. To tacklethis problem, a dueling double deep Q-network (D^{3}QN) based algorithm isinvoked for jointly determining the phase shift matrix and robots'trajectories. Specifically, the trajectories for robots contain a set of localoptimal positions, which reveals that robots make the optimal decision at eachstep. Numerical results demonstrated that the proposed D^{3}QN algorithmoutperforms the conventional algorithm, while the performance of IRS-NOMAnetwork is better than the orthogonal multiple access (OMA) network.

Trajectory and Passive Beamforming Design for IRS-aided Multi-Robot NOMA Indoor Networks