Age-of-Information based Multi-UAV Trajectories Using Deep Reinforcement Learning

Abstract

The use of unmanned aerial vehicles (UAVs) as a unified platform for sensing and communication is especially relevant in environments with inadequate infrastructure. In this paper, a multi-UAV system is constructed for dynamic data collection in a resource-constrained environment. The devised approach involves the implementation of an access platform referred to as the Access UAV (A_UAV). This A_UAV orchestrates the data collection process from Inspection-UAVs (I_UAVs), each equipped with a visual sensor, facilitating the relay of collected data to the cloud. Our approach jointly considers the trajectory scheduling of A_UAV and I_UAV to collect data from specific points in a decentralized manner. Specifically, a Deep Reinforcement Learning framework utilizing an actor-critic network is formulated for A_UAV, aiming to generate an equitable access schedule for I_UAVs. Moreover, trajectory scheduling of A_UAV ensures dynamic data collection while minimizing total system energy and the Age of Information (AoI) of data arriving from I_UAVs. The simulation results validate the performance of our proposed approach against several baselines under different parameter settings.

KEYWORDS:

• Access schedule
• Age of Information (AoI)
• data collection
• Deep Reinforcement Learning (DRL)
• multi-UAV system
• Unmanned Aerial Vehicles (UAVs)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Amanjot Kaur

Amanjot Kaur is currently enrolled in a joint PhD program at the Indian Institute of Technology (IIT), Ropar. She received her M-Tech (2015) from the Indian Institute of Technology (IIT), Jodhpur, India. Her research interests include multi-agent applications using UAVs and Multi-Agent Reinforcement Learning (MARL).

Shashi Shekhar Jha

Shashi Shekhar Shashi (M'12) received his PhD in computer science and engineering from the Indian Institute of Technology Guwahati, India in 2016. He was the recipient of TCS research fellowship during his PhD. He was a research scientist at the Fujitsu-SMU Urban Corp Lab., Singapore from 2016 to 2018. He is working as an assistant professor at the department of Computer Science and Engineering, Indian Institute of Technology Ropar, India since 2018. His current research interests include multiagent systems, multi-robot systems, reinforcement learning and artificial intelligence. Email: [email protected]