ORBITAL

ORBITAL — A Unified Planning Kernel for Aerospace Operations

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Inspiration

Modern aerospace mission planning is often fragmented across domain-specific tools. Aircraft and spacecraft systems are typically designed with separate planning architectures, even though they rely on similar optimization mechanics beneath the surface.

I was inspired to explore whether a single, reusable optimization kernel could support multiple constraint-critical domains without rewriting the planning logic each time. Instead of building separate planners, I wanted to design a modular system that separates decision variables, simulation, constraints, and objectives, creating a unified backend capable of generating executable mission plans across domains.

That exploration became ORBITAL.

What it does

ORBITAL is a unified mission planning framework designed for constraint-critical aerospace systems.

It generates physically executable mission plans by:

  1. Sampling decision variables from structured search spaces
  2. Simulating system behavior under domain-specific physics
  3. Enforcing hard feasibility constraints with margin discipline
  4. Penalizing soft constraint violations through weighted aggregation
  5. Optimizing mission performance objectives
  6. Evaluating robustness across multiple uncertainty scenarios

The architecture is domain-agnostic. The same optimization engine supports both UAV and CubeSat mission scenarios without duplicating planning logic.

ORBITAL includes an end-to-end execution pipeline that produces:

  • Executable mission plans
  • Constraint feasibility reports
  • Performance metrics
  • Robustness summaries
  • Visualization outputs
  • Structured artifacts (JSON, CSV, plots)

How we built it

ORBITAL was designed around four strict architectural separations:

1, Decision Space — Representation of controllable mission variables

  1. Simulation — Domain-specific system dynamics and physics models
  2. Constraints — Margin-based feasibility evaluation (hard vs soft)
  3. Objective — Unified scoring and penalty aggregation

The planner functions as a reusable optimization kernel. Each domain module defines its simulation and constraints but plugs into the same search, evaluation, and reporting framework.

Key implementation features include:

  • Margin-based constraint evaluation
  • Hard vs soft feasibility separation
  • Weighted penalty aggregation
  • Mutation-based search with controlled exploration
  • Robust scenario evaluation under uncertainty
  • Unified artifact reporting across domains

The result is a modular backend capable of scaling across mission types while maintaining strict feasibility transparency.

Challenges we ran into

One of the biggest challenges was maintaining strict separation between feasibility and optimization. Early versions blurred the relationship between constraint evaluation and scoring, reducing architectural clarity. Refining the lifecycle — sample, build, simulate, evaluate constraints, compute objective, aggregate penalties — required careful restructuring.

Another challenge was balancing simplicity with extensibility. More complex metaheuristics were considered, but the focus remained on clarity, modularity, and reproducibility.

Finally, translating a technically rigorous architecture into clear, accessible language for both technical and non-technical audiences required significant iteration.

Accomplishments that we're proud of

  • Designing a fully reusable, domain-agnostic optimization kernel
  • Demonstrating cross-domain applicability across UAV and CubeSat missions
  • Enforcing strict hard-constraint feasibility discipline
  • Implementing margin-based risk transparency reporting
  • Integrating robustness evaluation under uncertainty
  • Providing unified artifact generation and reproducible execution

Most importantly, ORBITAL demonstrates that physically executable mission plans can be generated across aircraft and spacecraft contexts without modifying the core planner.

What we learned

We learned that optimization is fundamentally structured decision-making under operational constraints.

Architectural separation is essential for scalability. When simulation, constraints, and objectives are entangled, systems become brittle. By isolating these concerns, ORBITAL remains extensible and reusable.

We also gained a deeper appreciation for feasibility discipline in safety-critical systems. Binary pass/fail checks are insufficient — margin-based evaluation provides meaningful insight into proximity to constraint violations.

Finally, we learned that communicating system architecture clearly is just as important as implementing it.

What's next for ORBITAL

Future development will focus on:

  • Scaling toward constellation-level mission coordination
  • Expanding robustness modeling under complex uncertainty
  • Parallelizing evaluation pipelines for faster optimization
  • Extending support to additional aerospace mission domains
  • Enhancing visualization and reporting capabilities

Long-term, ORBITAL aims to evolve into a scalable optimization backend capable of supporting high-density aerospace operations and enterprise-level decision workflows.

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