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CoAdapt: An LLM-based Framework for Adaptive Collaborative Perception in IIoT Robotic Swarms — arXiv:2609.16852

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.16852
License (as archived): CC0-1.0
Archived: 2026-10-15T18:32:15.121Z
SHA-256 of the archived text: ac330404eb8f75b3f96ff867deb7bcb9615363cb778659369dbb4ca2e9316ef3
Stable URL of this page: https://metraipolis.com/documento/dc_0413fd4e693e

How to cite

CoAdapt: An LLM-based Framework for Adaptive Collaborative Perception in IIoT Robotic Swarms — arXiv:2609.16852 — arXiv (en), https://arxiv.org/abs/2609.16852 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_0413fd4e693e (Metraipolis archive dc_0413fd4e693e, sha256 ac330404eb8f75b3f96ff867deb7bcb9615363cb778659369dbb4ca2e9316ef3)

BibTeX

@misc{dc0413fd4e693e,
  title = {CoAdapt: An LLM-based Framework for Adaptive Collaborative Perception in IIoT Robotic Swarms — arXiv:2609.16852},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.16852},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_0413fd4e693e; sha256 ac330404eb8f75b3f96ff867deb7bcb9615363cb778659369dbb4ca2e9316ef3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.16852. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Industrial IoT environments increasingly deploy autonomous mobile robots for tasks such as material handling, product assembly, or infrastructure inspection. In such deployments, collaborative perception enables robots to share LiDAR observations and collectively construct a richer model of their environment than an individual agent could produce alone. However, industrial environments are dynamic spaces where robot positions shift continuously, network bandwidth fluctuates, and the marginal contribution of robots to perception quality varies at runtime. Existing collaborative perception approaches are designed for static participation assumptions and cannot adapt to these dynamics without sacrificing either detection precision or communication efficiency. This paper presents CoAdapt, an adaptive collaborative perception framework for IIoT robotic swarms in which a Large Language Model (LLM) serves as a runtime fusion controller, jointly deciding which robots participate in the fusion process and which fusion algorithm to apply based on the current spatial configuration and network state. The LLM reasons over structured natural language descriptions of the scene derived from raw LiDAR point clouds, requiring no taskspecific training and generalizing to unseen swarm topologies. Evaluated on the OPV2V benchmark across 25 scenarios, our approach achieves a 38% reduction in communication cost while maintaining detection precision comparable to static baseline approaches.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-16 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.16852 — 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 2026-10-15T18:03:09.482Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-16?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).