Building Large-Scale Drone Defenses from Small-Team Strategies
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917272269881344 |
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| author | Douglas, Grant Franklin, Stephen Szabo, Claudia Guo, Mingyu |
| author_facet | Douglas, Grant Franklin, Stephen Szabo, Claudia Guo, Mingyu |
| contents | Defending against large adversarial drone swarms requires coordination methods that scale effectively beyond conventional multi-agent optimisation. In this paper, we propose to scale strategies proven effective in small defender teams by integrating them as modular components of larger forces using our proposed framework. A dynamic programming (DP) decomposition assembles these components into large teams in polynomial time, enabling efficient construction of scalable defenses without exhaustive evaluation. Because a unit that is strong in isolation may not remain strong when combined, we sample across multiple small-team candidates. Our framework iterates between evaluating large-team outcomes and refining the pool of modular components, allowing convergence on increasingly effective strategies. Experiments demonstrate that this partitioning approach scales to substantially larger scenarios while preserving effectiveness and revealing cooperative behaviours that direct optimisation cannot reliably discover. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_12502 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Building Large-Scale Drone Defenses from Small-Team Strategies Douglas, Grant Franklin, Stephen Szabo, Claudia Guo, Mingyu Multiagent Systems I.2.11; I.2.8 Defending against large adversarial drone swarms requires coordination methods that scale effectively beyond conventional multi-agent optimisation. In this paper, we propose to scale strategies proven effective in small defender teams by integrating them as modular components of larger forces using our proposed framework. A dynamic programming (DP) decomposition assembles these components into large teams in polynomial time, enabling efficient construction of scalable defenses without exhaustive evaluation. Because a unit that is strong in isolation may not remain strong when combined, we sample across multiple small-team candidates. Our framework iterates between evaluating large-team outcomes and refining the pool of modular components, allowing convergence on increasingly effective strategies. Experiments demonstrate that this partitioning approach scales to substantially larger scenarios while preserving effectiveness and revealing cooperative behaviours that direct optimisation cannot reliably discover. |
| title | Building Large-Scale Drone Defenses from Small-Team Strategies |
| topic | Multiagent Systems I.2.11; I.2.8 |
| url | https://arxiv.org/abs/2602.12502 |