Communication-Aware Path Design for Indoor Robots Exploiting Federated Deep Reinforcement Learning

Abstract

Robots have been widely used in the era of Inter-net of Things (IoT) and become an important driver behind 6G systems. However, due to the mobility of robots and the complicated wireless environment, it is challenging to control robots without prior knowledge, especially for the dynamic multi-robot systems. This paper investigates data transmission and path planning problems in an indoor multi-robot navigation system, where non-orthogonal multiple access (NOMA) is adopted to provide massive connectivity and improve spectrum efficiency. The considered system is designed for the long-term throughput maximization by jointly designing the downlink transmit power at the access point (AP) and the motion paths of the robots, while satisfying the power budget and mobility constraints. In this paper, a novel federated deep reinforcement learning approach called federated deep Q-network learning (F-DQN) algorithm is proposed to tackle the formulated problem. Simulation results demonstrate that the proposed algorithm not only speeds up the convergence rate and promotes the system throughput, but also is scalable for the number of robots.