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CoRelNav: Collaborative Relational Navigation for Multi-Robot Spatially Constrained Semantic Navigation — arXiv:2609.27720

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.27720
License (as archived): CC0-1.0
Archived: 2026-10-08T12:53:42.923Z
SHA-256 of the archived text: 7610f2998412f390c0513c30747263420f001e73ef89d2cb825d6bbf15946659
Stable URL of this page: https://metraipolis.com/documento/dc_43741abc2bc0

How to cite

CoRelNav: Collaborative Relational Navigation for Multi-Robot Spatially Constrained Semantic Navigation — arXiv:2609.27720 — arXiv (en), https://arxiv.org/abs/2609.27720 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_43741abc2bc0 (Metraipolis archive dc_43741abc2bc0, sha256 7610f2998412f390c0513c30747263420f001e73ef89d2cb825d6bbf15946659)

BibTeX

@misc{dc43741abc2bc0,
  title = {CoRelNav: Collaborative Relational Navigation for Multi-Robot Spatially Constrained Semantic Navigation — arXiv:2609.27720},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.27720},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_43741abc2bc0; sha256 7610f2998412f390c0513c30747263420f001e73ef89d2cb825d6bbf15946659},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.27720. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Spatially constrained semantic navigation requires robots to identify targets specified not only by semantic categories but also by relations to surrounding objects. In unknown environments, resolving such goals requires efficient exploration together with sufficient target and contextual evidence for reliable relation verification. Existing methods leave relation-aware verification and multi-robot collaboration largely disconnected: relational navigation is predominantly single-agent, while multi-robot systems seldom coordinate distributed observations for instance-specific relation verification. We propose CoRelNav, whose core is coupling task-conditioned multi-robot exploration with candidate-driven collaborative verification. A spatial-semantic field converts task constraints, scene nodes, and object features into exploration utility; as candidate information accumulates, robots are reallocated toward complementary evidence under team navigation costs, while instance-consistent observations are aggregated across topology nodes. This coupling reduces redundant search and enables relation hypotheses to be resolved from distributed partial evidence that independent exploration or isolated-view verification can leave ambiguous. Experiments in photorealistic simulation demonstrate consistent improvements over representative baselines, with ablations validating the proposed exploration and verification mechanisms. We further deploy the complete system on two physical mobile robots, demonstrating its applicability to real-world collaborative navigation.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-24 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.27720 — 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 2026-10-08T12:21:19.490Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-24?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).