Graph‑Based SLAM and Probabilistic Occupancy Grids for Efficient Multi‑Robot Underground Exploration in DARPA Subterranean Challenge — E8 Intelligence Research
Year: 2026
Type: preprint
Abstract: FINDING: Multi‑robot exploration in DARPA Subterranean Challenge relies on graph‑based SLAM and probabilistic occupancy grids for efficient coverage of unknown underground environments. | MATH: Occupancy probability \(P(o|z_{1:t})\) updated via Bayes' rule; graph Laplacian \(L = D - A\) for frontier detection; coverage metric \(C = \frac{|\bigcup_i \mathcal{O}_i|}{|\mathcal{U}|}\). | CONNECTION: Graph Laplacian eigenvalues relate to spectral clustering; no explicit golden ratio or base‑60 usage detected. | DEPTH: 6 FINDING: Proposal for a 21st‑century global mathematics library formalizes bibliographic metadata using category theory and graph databases, enabling automated theorem discovery. | MATH: Functorial mapping \(F: \mathcal{T} \to \mathcal{G}\) from theorem space to graph nodes; adjacency matrix \(A_{ij}\) encoding citation strength; similarity score \(s_{ij} = \frac{A_{ij}}{\sqrt{d_i d_j}}\). | CONNECTION: Graph embeddings may approximate hyperbolic space (negative curvature Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
Author: Caldin, Andrew Stewart
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225453931