Towards Reliable Identification and Tracking of Drones Within a Swarm

• Published in: Journal of intelligent & robotic systems Vol. 110; no. 2; p. 84
• Main Authors: Kumari, Nisha, Lee, Kevin, Barca, Jan Carlo, Ranaweera, Chathurika
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 05-06-2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Collision avoidance; Control; Coordination; Drones; Electrical Engineering; Engineering; Mechanical Engineering; Mechatronics; Monitoring; Regular Paper; Robotics; Tracking systems; Drone tracking; Computer vision; Swarm robotics; Machine learning; Real-Time surveillance; Multi object tracking; Object tracking; Vision based detection
• ISSN: 1573-0409; 0921-0296; 1573-0409
• DOI: 10.1007/s10846-024-02115-1

Drone swarms consist of multiple drones that can achieve tasks that individual drones can not, such as search and recovery or surveillance over a large area. A swarm’s internal structure typically consists of multiple drones operating autonomously. Reliable detection and tracking of swarms and individual drones allow a greater understanding of the behaviour and movement of a swarm. Increased understanding of drone behaviour allows better coordination, collision avoidance, and performance monitoring of individual drones in the swarm. The research presented in this paper proposes a deep learning-based approach for reliable detection and tracking of individual drones within a swarm using stereo-vision cameras in real time. The motivation behind this research is in the need to gain a deeper understanding of swarm dynamics, enabling improved coordination, collision avoidance, and performance monitoring of individual drones within a swarm. The proposed solution provides a precise tracking system and considers the highly dense and dynamic behaviour of drones. The approach is evaluated in both sparse and dense networks in a variety of configurations. The accuracy and efficiency of the proposed solution have been analysed by implementing a series of comparative experiments that demonstrate reasonable accuracy in detecting and tracking drones within a swarm.