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Autore principale: Ahaneku, Oguike
Natura: Recurso digital
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Pubblicazione: Zenodo 2026
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Accesso online:https://doi.org/10.5281/zenodo.19341064
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author Ahaneku, Oguike
author_facet Ahaneku, Oguike
contents <p>An exploratory geometric formalism is developed for autonomous navigation under sensor conflict. The purpose is not to claim a deployable navigation stack from a toy model, but to establish an exact witness principle: a partitioned sensor ensemble can be assigned a global structural fragmentation index based on its module count. The resulting intensive rate is strictly increasing in fragmentation and provides an exact global ordering of partition classes. The formalism supports three exact witness-level capabilities: (i) structural conflict classification through module-count-induced rate classes, (ii) exploratory trap-risk gating through high-fragmentation regimes, conditional on a suitable partition map, and (iii) exact inverse design of target structural classes. An illustrative 40-sensor ensemble defines low-, intermediate-, and high-fragmentation regimes. The resulting formalism should be read as a supervisory structural-risk witness that may complement, rather than replace, recursive estimators and path planners.</p>
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spellingShingle Geometric Autonomy as Structural Risk Gating: An Exploratory Constraint-Ensemble Witness for Sensor Conflict and Fragmentation
Ahaneku, Oguike
Autonomous navigation
Sensor fusion
Fragmentation index
Constraint ensemble
<p>An exploratory geometric formalism is developed for autonomous navigation under sensor conflict. The purpose is not to claim a deployable navigation stack from a toy model, but to establish an exact witness principle: a partitioned sensor ensemble can be assigned a global structural fragmentation index based on its module count. The resulting intensive rate is strictly increasing in fragmentation and provides an exact global ordering of partition classes. The formalism supports three exact witness-level capabilities: (i) structural conflict classification through module-count-induced rate classes, (ii) exploratory trap-risk gating through high-fragmentation regimes, conditional on a suitable partition map, and (iii) exact inverse design of target structural classes. An illustrative 40-sensor ensemble defines low-, intermediate-, and high-fragmentation regimes. The resulting formalism should be read as a supervisory structural-risk witness that may complement, rather than replace, recursive estimators and path planners.</p>
title Geometric Autonomy as Structural Risk Gating: An Exploratory Constraint-Ensemble Witness for Sensor Conflict and Fragmentation
topic Autonomous navigation
Sensor fusion
Fragmentation index
Constraint ensemble
url https://doi.org/10.5281/zenodo.19341064