A reproducible experimental framework for constrained Earth-observation scheduling.
Open the hosted research interface
From target geometry to a validated schedule, every decision remains reproducible, inspectable, and backed by the same simulation core.
Satellite scheduling demos often stop at a score. OrbitOps Lab keeps the whole evidence chain visible: a solver proposes task assignments, the discrete-event simulator reconstructs resource state, and the validator independently checks time windows, overlap, slew, energy, and storage constraints.
The algorithms, simulator, constraint system, benchmark harness, and linear Q-learning loop are implemented from scratch. Third-party libraries provide general infrastructure and 3D rendering—not the scheduling answers reported by the project.
| Explore | Optimize | Verify |
|---|---|---|
| CesiumJS/WGS84 target geometry and observation sequence | 11 baseline, exact, stochastic-search, and learning solvers | One shared simulator, explainable violations, and reproducible artifacts |
| Visibility-aware mission Gantt chart | Seeded budgets and convergence traces | Energy/storage envelopes and feasibility verdicts |
| Q-learning objective, epsilon, and TD-error curves | Branch-and-bound optimality on small instances | Golden, property, unit, and integration tests |
- 3D mission geometry: an interactive CesiumJS globe places every WGS84 target on NASA Blue Marble imagery, highlights selected observations, draws the scheduled target sequence, and provides an explicitly notional orbit-context track. It requires no Cesium ion token, retains a Natural Earth fallback, and sends no scenario data to a hosted scheduler.
- Mission Gantt: every target receives a row containing all committed visibility windows, the selected observation interval, and its preceding slew.
- Resource envelope: energy remaining and storage consumed are replayed from the authoritative simulator after each observation.
- Comparative evidence: the same scenario can be inspected across methods using objective value, completion, slew, runtime, evaluation count, and stop-reason columns rather than isolated score cards.
- Constraint audit: every run exposes resource minima, binding margins, omitted-task reasons, exact-search proof status, and stochastic search effort when those diagnostics are available.
- Learning diagnostics: Q-learning runs expose exploratory episode-schedule objective, exploration decay, and normalized mean absolute temporal-difference error; pure replayable policy results are separated from the greedy-backed hybrid.
- Honest boundary: the globe is a mission-context view. High-fidelity orbit propagation is deliberately outside v0.1 and is never implied by the display.
python3 -m venv .venv
source .venv/bin/activate
python -m pip install -e '.[dev]'
orbitops lab --scenarios scenariosOpen http://127.0.0.1:8000. The Web Lab includes deterministic 6, 10, 18, and
30-task showcase scenarios, method comparison, a 3D mission view, Gantt and
resource envelopes, constraint audit, and learning curves.
The GitHub Pages interface provides a serverless reproducibility view. Its committed results use deterministic, size-aware budgets and report every actual seed, budget, stop reason, omission, and build provenance. Local execution remains the authoritative mode for arbitrary seeds, budgets, and new scenarios.
The same domain core is available from the command line:
orbitops validate scenarios/examples/demo.json
orbitops solve scenarios/examples/demo.json --solver genetic --seed 42 --evaluation-budget 500
orbitops solve scenarios/tiny/tiny-conflict.json --solver branch-and-bound
orbitops benchmark configs/benchmark-smoke.toml --output runs/benchmark-smoke
orbitops train scenarios/examples/demo.json --model-output runs/demo-policy.json --episodes 250
pytest| Family | Implementations | Evidence |
|---|---|---|
| Baseline | random feasible, value, value density, deadline, global insertion | Deterministic contracts and feasibility replay |
| Exact | exhaustive search, branch-and-bound | Optimality on bounded tiny scenarios |
| Search | multi-start local search, genetic algorithm | Seeded evaluation budgets and convergence traces |
| Learning | pure-policy replay and greedy-backed linear Q-learning | Versioned policy JSON, training trace, fingerprint-checked replay |
The objective is lexicographic: maximize total priority value, then completed task count, then minimize total slew time. Every solver returns decisions through the same contract and is scored by the same independent simulation path.
- Immutable, versioned Pydantic models at package boundaries.
- Canonical Scenario, Schedule, and learned-policy JSON Schemas.
- Seeded stochastic solvers, benchmark campaigns, and learning experiments.
- Atomic checkpoints, resumable parallel campaigns, and reproducibility fingerprints for reports and scenario-bound policies.
- Scenario-block bootstrap confidence intervals and paired win/tie/loss comparisons on normalized, scale-comparable outcomes.
- Strict mypy, Ruff, unit, integration, property, and golden-test coverage.
- Standalone benchmark reports that contain no plotting-framework dependency.
packages/orbitops/
├── domain/ # immutable contracts and lexicographic objective
├── simulation/ # transitions, event replay, resources, validation
├── solvers/ # baseline, exact, search, and Q-learning solvers
├── learning/ # environment, trainer, policy artifact
├── benchmarking/ # scenario generation, campaigns, aggregation
├── reporting/ # standalone HTML benchmark evidence
└── web/ # local API and interactive mission lab
scenarios/ # committed reproducible inputs
schemas/ # versioned JSON contracts
tests/ # unit, integration, property, and golden tests
docs/ # formulation, algorithms, architecture, and evidence
v0.2 models one agile satellite, multiple observation targets, offline planning, precomputed visibility windows, attitude slew, energy, and storage. Downlink planning, multi-satellite coordination, high-resolution terrain, time-varying weather layers, and high-fidelity orbit propagation are deliberate future extensions.
Start with the problem formulation, then see the simulation model, algorithms, benchmarking, visual reports, Web Lab, and reinforcement learning.
Release scope: v0.2 retains the versioned v0.1 single-satellite model contract while adding 11 solver modes, complex comparative scenarios, constraint audit, paired statistical evidence, resumable benchmarks, and integrity-checked experiment artifacts. Orbit propagation remains an explicit future model-contract extension rather than a visual claim.
Policy artifact migration: v0.2 intentionally moves linear Q-policy files to schema v2 because resource-headroom feature semantics changed. Retrain v1 artifacts with v0.2; old weights are rejected and are not silently migrated.