SwarmRaft: Leveraging Consensus for Robust Drone Swarm Coordination in GNSS-Degraded Environments
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911480532697088 |
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| author | Dev, Kapel Madhwal, Yash Shevelo, Sofia Osinenko, Pavel Yanovich, Yury |
| author_facet | Dev, Kapel Madhwal, Yash Shevelo, Sofia Osinenko, Pavel Yanovich, Yury |
| contents | Unmanned aerial vehicle (UAV) swarms are increasingly used in critical applications such as aerial mapping, environmental monitoring, and autonomous delivery. However, the reliability of these systems is highly dependent on uninterrupted access to the Global Navigation Satellite Systems (GNSS) signals, which can be disrupted in real-world scenarios due to interference, environmental conditions, or adversarial attacks, causing disorientation, collision risks, and mission failure. This paper proposes SwarmRaft, a blockchain-inspired positioning and consensus framework for maintaining coordination and data integrity in UAV swarms operating under GNSS-denied conditions. SwarmRaft leverages the Raft consensus algorithm to enable distributed drones (nodes) to agree on state updates such as location and heading, even in the absence of GNSS signals for one or more nodes. In our prototype, each node uses GNSS and local sensing, and communicates over WiFi in a simulated swarm. Upon signal loss, consensus is used to reconstruct or verify the position of the failed node based on its last known state and trajectory. Our system demonstrates robustness in maintaining swarm coherence and fault tolerance through a lightweight, scalable communication model. This work offers a practical and secure foundation for decentralized drone operation in unpredictable environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00622 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SwarmRaft: Leveraging Consensus for Robust Drone Swarm Coordination in GNSS-Degraded Environments Dev, Kapel Madhwal, Yash Shevelo, Sofia Osinenko, Pavel Yanovich, Yury Distributed, Parallel, and Cluster Computing Unmanned aerial vehicle (UAV) swarms are increasingly used in critical applications such as aerial mapping, environmental monitoring, and autonomous delivery. However, the reliability of these systems is highly dependent on uninterrupted access to the Global Navigation Satellite Systems (GNSS) signals, which can be disrupted in real-world scenarios due to interference, environmental conditions, or adversarial attacks, causing disorientation, collision risks, and mission failure. This paper proposes SwarmRaft, a blockchain-inspired positioning and consensus framework for maintaining coordination and data integrity in UAV swarms operating under GNSS-denied conditions. SwarmRaft leverages the Raft consensus algorithm to enable distributed drones (nodes) to agree on state updates such as location and heading, even in the absence of GNSS signals for one or more nodes. In our prototype, each node uses GNSS and local sensing, and communicates over WiFi in a simulated swarm. Upon signal loss, consensus is used to reconstruct or verify the position of the failed node based on its last known state and trajectory. Our system demonstrates robustness in maintaining swarm coherence and fault tolerance through a lightweight, scalable communication model. This work offers a practical and secure foundation for decentralized drone operation in unpredictable environments. |
| title | SwarmRaft: Leveraging Consensus for Robust Drone Swarm Coordination in GNSS-Degraded Environments |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2508.00622 |