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

This paper aims to solve the optimization problems in far-field wireless power transfer systems using deep reinforcement learning techniques. The Radio-Frequency (RF) wireless transmitter is mounted on a mobile robot, which patrols near the harvested energy-enabled Internet of Things (IoT) devices....

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Bibliographic Details
Published in:Wireless power transfer 2022, Vol.2022 (1), p.9921885-9921885
Main Authors: Xing, Yuan, Young, Riley, Nguyen, Giaolong, Lefebvre, Maxwell, Zhao, Tianchi, Pan, Haowen, Dong, Liang
Format: Article
Language:English
Subjects:
Algorithms
Antennas
Deep learning
Efficiency
Internet of Things
Machine learning
Optimization
Path planning
Radio frequency
Receivers & amplifiers
Robots
Transmitters
Unmanned aerial vehicles
Wireless power transmission
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Summary:This paper aims to solve the optimization problems in far-field wireless power transfer systems using deep reinforcement learning techniques. The Radio-Frequency (RF) wireless transmitter is mounted on a mobile robot, which patrols near the harvested energy-enabled Internet of Things (IoT) devices. The wireless transmitter intends to continuously cruise on the designated path in order to fairly charge all the stationary IoT devices in the shortest time. The 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. The 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 closed-loop path for the robot to cruise on, which can enable the robot to continuously charge all the IoT devices in the shortest time. The numerical results prove the superiority of the AD-DQN in optimizing the proposed wireless power transfer system.
ISSN:2052-8418
2052-8418
DOI:10.1155/2022/9921885