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Neuromorphic Decision Dynamics for Deadlock-free Safe Navigation in Congested Environments — arXiv:2610.33271

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.33271
License (as archived): CC0-1.0
Archived: 2026-10-27T07:55:23.203Z
SHA-256 of the archived text: 04541f35cc6146f17fb66d6e5bf1c43ebeb90756afa573a381c2d7dc55ba66cd
Stable URL of this page: https://metraipolis.com/documento/dc_669c6b8cbfa6

How to cite

Neuromorphic Decision Dynamics for Deadlock-free Safe Navigation in Congested Environments — arXiv:2610.33271 — arXiv (en), https://arxiv.org/abs/2610.33271 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_669c6b8cbfa6 (Metraipolis archive dc_669c6b8cbfa6, sha256 04541f35cc6146f17fb66d6e5bf1c43ebeb90756afa573a381c2d7dc55ba66cd)

BibTeX

@misc{dc669c6b8cbfa6,
  title = {Neuromorphic Decision Dynamics for Deadlock-free Safe Navigation in Congested Environments — arXiv:2610.33271},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.33271},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_669c6b8cbfa6; sha256 04541f35cc6146f17fb66d6e5bf1c43ebeb90756afa573a381c2d7dc55ba66cd},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.33271. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Safe navigation in congested environments requires both collision avoidance and coordinated passage. We introduce neuromorphic decision dynamics (NDD), a decentralized, event-based controller, that provides navigation in congested spaces with guarantees on safety, liveness, robustness, and scalability. NDD makes safe-and-live deconfliction a controllable, locally tunable emergent property of local agent interactions, without requiring communication, preassigned priorities, models of neighbors' objectives or future actions, or estimates of group size or global network properties. By design, NDD couples the dynamics of internal decision and attention states with changing physical states and locally observed relative motion. Each agent continuously modulates its speed along its prescribed path, responding as soon and as much as conflict resolution requires. Agents self-organize into disjoint, locally engaged groups that resolve conflicts concurrently. When any group's conflict becomes safety-critical, decision-action feedback destabilizes collective indecision, and coordinated conflict-resolution emerges that stabilizes a safe traversal order within the group. Order persists as conflicts clear, but groups may split or merge as agents autonomously adapt to new conflicts and those with resolved conflicts resume their nominal motion. We derive explicit conditions on sensing range, feedback gains, response time scales, and actuation limits that guarantee collision avoidance and traversal-order formation in finite time with no deadlock or livelock for finite, heterogeneous agent populations of arbitrary, changing size. Computational cost scales with the number of local interactions sensed by each agent. Large-scale simulations, comparisons with existing approaches, and hardware experiments demonstrate NDD in complex congested scenarios and validate its scalability.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.MA, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO) ; Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2610.33271 — 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 2026-10-27T07:55:15.051Z dalla pagina https://arxiv.org/list/cs.MA/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).