Nexus of Deep Reinforcement Learning and Leader–Follower Approach for AIoT Enabled Aerial Networks

Abstract

The Industrial Internet of Things (IIoT) is a new industrial 4.0 paradigm that combines IoT, robotics, cyber-physical systems, and other future industrial advancements. Unmanned Aerial Vehicles (UAVs), part of the IIoT infrastructure, have a significant potential for civil and military purposes. Through the Artificial Intelligence of Things (AIoT), a well-organized group of UAVs outperforms a single large UAV in terms of device scalability, maintenance, and expense. Therefore, the UAV swarm with industry 4.0 intelligence can be used for a wide range of 24/7 security and remote monitoring applications. Though multi-UAV systems are beneficial, their application has many challenges. There is a high risk of collision in the multi-UAV system without coordination. This paper proposes an AIoT-based Navigation and Formation Control (AIoT-NFC) mechanism to scale down the collision risk by combining Deep Reinforcement Learning (DRL) with the leader-follower approach. In AIoT-NFC, a Deep Deterministic Policy Gradient (DDPG) based algorithm is proposed to navigate UAVs in remote surveillance without colliding with obstacles and other UAVs. Furthermore, the AIoT-NFC system incorporates a fault tolerance mechanism that can handle the scenario of a leader's failure due to actuator malfunction. Experimental results show that the AIoT-NFC achieves faster convergence with a lower collision rate. AIoT-NFC reduced the collision rate by 14.99% compared to existing navigation methods in successful formation without colliding with the other UAVs.