ΔΦ GUIDED NAVIGATION ENGINE v4.0 Field-Driven Swarm Coordination, Coherence Emergence, and Cytoskeletal Transport Analog

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Auteur principal: Mitchell , Thomas S.
Format: Recurso digital
Publié: Zenodo 2026
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author Mitchell , Thomas S.
author_facet Mitchell , Thomas S.
contents <p>The ΔΦ Guided Navigation Engine v4.0 presents a field-based swarm coordination system demonstrating emergent path formation, reinforcement-driven transport, and decentralized navigation. Implemented on a microtubule-inspired 13-protofilament lattice, the system bridges computational swarm behavior with biological transport analogs. The framework integrates gradient dynamics, trail reinforcement, evaporation decay, and coherence emergence, providing a unified model for distributed coordination.</p>
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_19081481
institution Zenodo
language
publishDate 2026
publisher Zenodo
record_format zenodo
spellingShingle ΔΦ GUIDED NAVIGATION ENGINE v4.0 Field-Driven Swarm Coordination, Coherence Emergence, and Cytoskeletal Transport Analog
Mitchell , Thomas S.
ΔΦ, Mitchell Equation, swarm intelligence, microtubules, cytoskeleton, navigation engine, emergent behavior, coherence, Orch-OR, field dynamics, decentralized systems, path optimization
<p>The ΔΦ Guided Navigation Engine v4.0 presents a field-based swarm coordination system demonstrating emergent path formation, reinforcement-driven transport, and decentralized navigation. Implemented on a microtubule-inspired 13-protofilament lattice, the system bridges computational swarm behavior with biological transport analogs. The framework integrates gradient dynamics, trail reinforcement, evaporation decay, and coherence emergence, providing a unified model for distributed coordination.</p>
title ΔΦ GUIDED NAVIGATION ENGINE v4.0 Field-Driven Swarm Coordination, Coherence Emergence, and Cytoskeletal Transport Analog
topic ΔΦ, Mitchell Equation, swarm intelligence, microtubules, cytoskeleton, navigation engine, emergent behavior, coherence, Orch-OR, field dynamics, decentralized systems, path optimization
url https://doi.org/10.5281/zenodo.19081481