pith:KOSUZFO4
Informative Path Planning with Guaranteed Estimation Uncertainty
The shortest path for a robot to measure an environmental field can be computed so that Gaussian process posterior variance stays below any chosen threshold everywhere in the region.
arxiv:2602.05198 v3 · 2026-02-05 · cs.RO
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Record completeness
Claims
computing the shortest path whose measurements ensure that the Gaussian process (GP) posterior variance -- an intrinsic uncertainty measure that lower-bounds the mean-squared prediction error under the GP model -- is upper bounded by a user-specified threshold over the monitoring region
The GP model learned from prior data accurately captures the true spatial correlations of the environmental field so that posterior variance provides a valid bound on prediction error; this is invoked when transforming the kernel into coverage maps.
A method computes near-shortest paths guaranteeing GP posterior variance below a threshold over a region by converting kernels to binary coverage maps and solving a budgeted routing problem with approximation guarantees.
Formal links
Receipt and verification
| First computed | 2026-05-28T01:04:07.810707Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
53a54c95dcf2c0e4c417bf5722eb362e638ba181b39e741d3e628ce5a4292c12
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/KOSUZFO46LAOJRAXX5LSF2ZWFZ \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 53a54c95dcf2c0e4c417bf5722eb362e638ba181b39e741d3e628ce5a4292c12
Canonical record JSON
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