An Efficient Formation Control mechanism for Multi-UAV Navigation in Remote Surveillance

Gunasekaran Raja, Yashvandh Baskar, Priyanka Dhanasekaran, Raheel Nawaz, Keping Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


Multiple Unmanned Aerial Vehicles (UAVs) have a greater potential to be widely used in civil and military applications. Swarm of UAVs can be deployed in a multitude of 24/7 security and surveillance. The network management and pattern formation are crucial for multi-UAV formation control mechanisms while cautiously navigating the surveillance areas. A Deep Reinforcement Learning (DRL) based Formation Flight Control for Navigation (FFCN) is used to efficiently build the UAV swarm, which decreases networking load by minimizing communication and processing involved in pattern formation. Moreover, through the leader-follower navigation, the network management of the swarm is substantially simplified. The leader-follower approach in FFCN is efficient for multi-UAV as the navigation system needs to find only the leader's trajectory. However, the failure of the leader due to actuator faults decreases the efficiency of the system. The proposed FFCN addresses the above by including a fault-tolerance mechanism, thus improving the system's reliability. Simulation results show that the FFCN model achieves faster convergence in less time with a lower collision rate. The model's usage reduced the collision rate to 3.4% in successful formation without colliding with other UAVs.

Original languageEnglish
Title of host publication2021 IEEE Globecom Workshops, GC Wkshps 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665423908
Publication statusPublished - 2021
Event2021 IEEE Globecom Workshops, GC Wkshps 2021 - Madrid, Spain
Duration: 2021 Dec 72021 Dec 11

Publication series

Name2021 IEEE Globecom Workshops, GC Wkshps 2021 - Proceedings


Conference2021 IEEE Globecom Workshops, GC Wkshps 2021


  • Collision Avoidance
  • Fault-Tolerance and Deep Q Network (DQN)
  • Pattern Formation
  • Remote Surveillance
  • Unmanned Aerial Vehicles (UAVs)

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing
  • Software
  • Information Systems and Management
  • Artificial Intelligence
  • Computer Networks and Communications


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