pith:W2XVKBDZ
Stochastic Differential Dynamic Programming for Trajectory Optimization under Partial Observability
A stochastic differential dynamic programming algorithm optimizes spacecraft trajectories under partial observability by jointly handling control and belief-state evolution.
arxiv:2605.07529 v2 · 2026-05-08 · eess.SY · cs.SY · math.OC
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Claims
the proposed method can exploit the coupling between trajectory design and orbit determination to obtain navigation-aware solutions with substantially lower fuel consumption than those from deterministic local optimization starting from the same initial guess
The belief dynamics accurately capture the dependence of covariance propagation on the nominal trajectory and that the stochastic differential dynamic programming iterations converge to a solution that respects all general mission constraints without separation of estimation and control
A new stochastic differential dynamic programming method optimizes coupled trajectory design and orbit determination under partial observability, producing navigation-aware solutions with lower fuel consumption than deterministic local optimization in examples like the circular restricted three-body
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| First computed | 2026-06-10T01:10:03.125782Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
b6af55047961d9b37160b9e76952d57c01aa081470e9f493f1dba75895802bdb
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/W2XVKBDZMHM3G4LAXHTWSUWVPQ \
| 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())"
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Canonical record JSON
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