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Geometric Coherence via Weighted Matching for 3D Heterogeneous Multi-Agent Reach-Avoid Games — arXiv:2610.06882

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.06882
License (as archived): CC0-1.0
Archived: 2026-10-07T13:11:37.501Z
SHA-256 of the archived text: ff28d165e566151761db177010e2f52817206c97dd3bc910fa9408b1d6e672b3
Stable URL of this page: https://metraipolis.com/documento/dc_526ba720aaa3

How to cite

Geometric Coherence via Weighted Matching for 3D Heterogeneous Multi-Agent Reach-Avoid Games — arXiv:2610.06882 — arXiv (en), https://arxiv.org/abs/2610.06882 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_526ba720aaa3 (Metraipolis archive dc_526ba720aaa3, sha256 ff28d165e566151761db177010e2f52817206c97dd3bc910fa9408b1d6e672b3)

BibTeX

@misc{dc526ba720aaa3,
  title = {Geometric Coherence via Weighted Matching for 3D Heterogeneous Multi-Agent Reach-Avoid Games — arXiv:2610.06882},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.06882},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_526ba720aaa3; sha256 ff28d165e566151761db177010e2f52817206c97dd3bc910fa9408b1d6e672b3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.06882. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We study assignment quality in 3D heterogeneous multi-agent reach-avoid games and identify a recurring failure mode of cardinality-only matching in geometrically structured scenarios, which we term \emph{Geometric Sprawl}. In these cases, multiple maximum-cardinality assignments are available, but some induce spatially incoherent pairings and inefficient pursuit trajectories. Building on the evasion-space framework of Yan et al.~\cite{yan2022}, we introduce a cardinality-first weighted sequential matching method in which the Hamilton--Jacobi--Isaacs interception value $z_I(s,j)$ is used as a secondary assignment weight. Each sequential stage is solved with a min-cost max-flow backend, while the unweighted baseline uses the same solver with the weight term removed. We evaluate both methods on a deterministic 35-scenario benchmark (7 families & 5 initialization variants) under two regimes: a diagnostic stationary-unmatched setting and a hybrid saddle-point setting with goal-directed unmatched evaders. On this benchmark, the weighted method resolves 13 of 15 geometric stress-test instances that cause repeated timeouts for the unweighted baseline in the diagnostic regime, and under the hybrid regime improves mean captures from $3.20$ to $3.91$ (22\% increase) and mean interception height from $3.87$ to $5.36$ (40\% increase). We also report lower path tortuosity and lower angular-effort proxy values, suggesting smoother pursuit trajectories in this first-order simulation model. We release the simulator and benchmark suite at \href{ this https URL }{ this http URL } to support reproducible evaluation of assignment strategies for 3D reach-avoid games.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.RO, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2610.06882 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T12:42:33.482Z dalla pagina https://arxiv.org/list/cs.RO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).