Optimal Path Planning for Wireless Power Transfer Robot Using Area Division Deep Reinforcement Learning

Abstract

is paper aims to solve the optimization problems in far-field wireless power transfer systems using deep reinforcement learning techniques. e Radio-Frequency (RF) wireless transmitter is mounted on a mobile robot, which patrols near the harvested energy-enabled Internet of ings (IoT) devices. e wireless transmitter intends to continuously cruise on the designated path in order to fairly charge all the stationary IoT devices in the shortest time. e Deep Q-Network (DQN) algorithm is applied to determine the optimal path for the robot to cruise on. When the number of IoT devices increases, the traditional DQN cannot converge to a closed-loop path or achieve the maximum reward. In order to solve these problems, an area division Deep Q-Network (AD-DQN) is invented. e algorithm can intelligently divide the complete charging field into several areas. In each area, the DQN algorithm is utilized to calculate the optimal path. After that, the segmented paths are combined to create a closedloop path for the robot to cruise on, which can enable the robot to continuously charge all the IoT devices in the shortest time. e numerical results prove the superiority of the AD-DQN in optimizing the proposed wireless power transfer system.