Building My Own Astrodynamics Library In Zig Link to heading

I have worked in the aerospace world for the past 8 years. In that time, I have picked up and worked on the some of the truly complex and precise pieces of software. However, something I really haven’t been able to spend a ton of time on is the world of astrodynamics. I wanted to learn more about what went into orbit propagation and the mechanics of body to body transfers. The physics of making these systems as predictable as possible seems quite complex and it was something that I found to be interesting enough to me to pursue outside of work. That is when I decided to embark on building astroz - my own astrodynamics and spacecraft toolkit written from the ground up in Zig. This started primarily as a learning exercise to understand the underlying mathematics and algorithms, but has evolved into something that could potentially be useful for others.

Why Zig for Astrodynamics? Link to heading

Existing astrodynamics libraries often feel like black boxes. You aren’t always sure what is happening behind the scenes and I wanted to peek behind the curtain and see if I could discover what makes these tick and how I can implement my own solutions.

Choosing Zig wasn’t just about trying the latest shiny language - it was about learning both a new systems programming language and astrodynamics fundamentals simultaneously. Astrodynamics computations are inherently performance-critical - when you’re propagating orbits for thousands of objects or processing real-time telemetry data, accuracy and performance counts. Zig’s promise of C-level performance with modern safety features made it an attractive choice for me to explore. While not quite “ready” for production, I wanted to build something in a language that I have been watching for some time, and this seemed like a great opportunity to give Zig a shot at handling a complex project.

The language’s explicit memory management gives me control over allocations without the overhead of a garbage collector, which is crucial when dealing with continuous numerical integrations. Plus, Zig’s compile-time evaluation capabilities turned out to be perfect for pre-computing mathematical constants and lookup tables that astrodynamics algorithms heavily rely on.

Learning by Building Link to heading

While there are excellent mature astrodynamics libraries available, I chose to build from scratch because I learn best by implementing algorithms myself. Reading about orbital propagation is one thing; debugging and working through issues that arise after a week of simulation taught me the nuances better than a textbook could.

What astroz Brings to the Table Link to heading

Building astroz from scratch allowed me to create a comprehensive toolkit that covers the full spectrum of spacecraft operations:

Orbital Mechanics Core: The foundation includes classical orbital propagation using RK4 integration, support for various orbital maneuvers (impulse, phase changes, plane changes), and TLE (Two-Line Element) parsing for real-world satellite tracking. Additionally, included in the foundation is Hohmann Transfers which allows for interplanetary mission planning around an orbiting body. This was arguably the most challenging part of this entire library for me. It was things I had to spend the time and research the various ways these things happen. I recommend checking out orbital mechanics; it has some great examples in python and has pages dedicated to explaining the math behind some of these complex problems.

Communication Standards: One area where I wanted astroz to shine was in handling spacecraft communication protocols. This is an area that I have worked in deeply in my job, and feel like I have a very large amount of expertise in. I wanted it to not only be functionality sound, but also practical enough to be used in a production environment one day. The library includes parsers for CCSDS (Consultative Committee for Space Data Systems) packets and streams, as well as VITA49 radio frequency data packets - standards that are essential for real spacecraft operations but often overlooked in academic astrodynamics libraries. I wanted to build a “one stop shop” of sorts for most things you may need when working with spacecraft data.

Astronomical References: Beyond basic orbital mechanics, astroz provides robust support for astronomical coordinate systems (equatorial, world coordinate system), celestial body data (masses, radii, orbital parameters), and time systems including J2000 and Julian Date conversions.

Data Processing: The library includes FITS file parsing capabilities for astronomical imaging data, though I’ll be honest - this feature is currently broken due to dependency issues with zigimg (Hopefully I will revisit this soon and this will be working again).

The Challenges of Building from Scratch Link to heading

Numerical Precision and Stability Link to heading

One of the first major hurdles was implementing numerically stable orbital propagation. Unlike a simple physics simulation, orbital mechanics requires maintaining precision over very long time periods. A tiny numerical error in your integrator can compound over thousands of orbital periods, leading to completely wrong trajectories.

I learned this the hard way when my initial RK4 implementation worked perfectly for short propagations but completely fell apart when trying to propagate a satellite’s orbit for a week. The solution involved implementing variable step size control and carefully managing numerical precision throughout the integration pipeline. I found myself reading lots of reference material on how to optimize RK4 for spacecraft orbit propagations and tried to fold them into my library.

Coordinate System Transformations Link to heading

Astrodynamics involves a dizzying array of coordinate systems - Earth-Centered Inertial (ECI), Earth-Centered Earth-Fixed (ECEF), topocentric, and various celestial reference frames. Each transformation must be mathematically precise, and small errors can lead to satellites appearing to be on the wrong side of the planet.

The challenge wasn’t just implementing the transformations correctly, but designing an API that makes it difficult for users to accidentally mix coordinate systems. Zig’s strong type system helped here - I could create distinct types for positions in different reference frames, preventing runtime errors that plague many astrodynamics codebases, especially when first starting out with the library.

Protocol Parsing Without Existing Libraries Link to heading

Building CCSDS and VITA49 parsers from scratch meant implementing binary protocol parsing with careful attention to endianness, bit packing, and error handling. These protocols are used in real spacecraft operations, so compatibility and robustness were non-negotiable.

The documentation for these standards is comprehensive but dense, and translating specification documents into working code requires patience and meticulous testing. I found myself writing extensive test suites using real packet captures to ensure compatibility. Thankfully I felt the most confident in this area since I have done with kind of work before (in rust not zig) so I had more confidence building out this than the other pieces of the toolkit.

Dependency Management and Ecosystem Challenges Link to heading

Zig’s ecosystem is still young, which is both a blessing and a curse. On one hand, there’s less dependency bloat and fewer compatibility headaches. On the other hand, basic functionality that you’d take for granted in mature ecosystems sometimes doesn’t exist. This means I often had to build my own tools to and packages to get things working that I wanted. This was something I hadn’t particularly done before and I was a fun and challenging exercise to implmement some of the common things we often find ourselves just importing in other languages.

Lessons Learned Link to heading

Start with the Fundamentals Link to heading

I initially tried to build too many features at once. The breakthrough came when I focused on getting basic two-body orbital propagation rock-solid before moving on to perturbations and complex maneuvers. A strong foundation in the core algorithms made everything else easier, and robust enough that I didn’t have to keep circling back and updating exisiting code to fit my new math.

Test Against Known Solutions Link to heading

Astrodynamics has the advantage of having well-established test cases and benchmark problems. I found myself constantly comparing astroz’s output against established libraries like GMAT and published orbital mechanics textbooks. This validation process caught subtle bugs that would have been nearly impossible to find otherwise.

Performance Matters, But Correctness Comes First Link to heading

While Zig’s performance was a major motivation, I learned that getting the math right is far more important than micro-optimizations. A blazingly fast orbital propagator that gives wrong answers is useless. Once the algorithms were correct, then I could focus on optimization. This is still a work in progress for sure, but I am happy with the speed I get out of the solver currently.

Documentation and API Design Are Critical Link to heading

Astrodynamics concepts are complex enough without a confusing API making things worse. I spent considerable time designing function signatures and data structures that would be intuitive to users familiar with orbital mechanics concepts, even if it meant more verbose code internally. I wanted to spell things out, not just for users of the library, but also for myself. I wanted to keep the code as transparent as possible and trying to avoid the pitfall from other libraries where things get so abstracted away they become difficult to debug.

The Road Ahead Link to heading

Building astroz has been an incredible learning experience that deepened my understanding of both astrodynamics and systems programming. The library is still evolving - I’m working on fixing the FITS parsing issues, adding more sophisticated perturbation models, and expanding the astronomical coordinate system support.

The goal was never to replace established libraries like Orekit or poliastro - this started as a personal learning project to deepen my understanding of both astrodynamics and systems programming. However, the resulting library has grown into something that could provide a modern, high-performance alternative…

For anyone considering building their own specialized scientific computing library, my advice is simple: start small, test extensively, and don’t be afraid to make mistakes. The journey of building astroz taught me more about astrodynamics than any textbook ever could, and the resulting library - while still growing - is capable of handling mission critical computations that I could use in my future work.

The source code is available at github.com/ATTron/astroz, and I welcome contributions from anyone interested in the intersection of modern systems programming and space science. If you want to keep in touch I have my socials listed on the homepage, and you can subscribe to my RSS feed