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Towards Reliable Identification and Tracking of Drones Within a Swarm.
- Source :
- Journal of Intelligent & Robotic Systems; Jun2024, Vol. 110 Issue 2, p1-31, 31p
- Publication Year :
- 2024
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09210296
- Volume :
- 110
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Journal of Intelligent & Robotic Systems
- Publication Type :
- Academic Journal
- Accession number :
- 177732829
- Full Text :
- https://doi.org/10.1007/s10846-024-02115-1