Why I’m Building at the Intersection of AI and Space Technology

Why I’m Building at the Intersection of AI and Space Technology

Technology has always fascinated me, but two fields keep pulling me back more than anything else:Artificial Intelligence and Space Technology

At first, they seemed like two completely different worlds.

One deals with algorithms, data, models, and intelligent decision-making.

The other deals with spacecraft, orbital mechanics, satellites, robotics, and exploring environments far beyond Earth.

The more I started learning, however, the more I realized something:

The future of space exploration will increasingly depend on intelligent systems.

And that intersection is exactly where I want to build.

A Little About Me

I'm a Computer Science student at VIT Chennai, while also studying Aeronautics and Space Technology through IIT Madras.

My interests currently span:

• Artificial Intelligence and Machine Learning
• Space Technology
• Autonomous Systems
• Quantum Computing
• Competitive Programming
• Robotics and Sensor Fusion
• Research

That's a lot of fields — and I'm definitely not an expert in all of them.

I'm learning.

I'm experimenting.

And most importantly, I'm building.

This blog is where I want to document that process.

Why Artificial Intelligence?

What fascinates me about AI isn't simply the ability to train a model.

I'm much more interested in the question:

How can we build systems that make useful decisions when the real world is uncertain?

Real engineering problems rarely provide perfect information.

Sensors have noise.

Predictions have uncertainty.

Conditions change.

Sometimes several objectives have to be balanced simultaneously.

That makes areas such as machine learning, anomaly detection, explainable AI, optimization and autonomous decision-making** particularly interesting to me.

And then I started thinking about where these capabilities could have some of their most exciting applications.

That led me to space.

Why Space Technology?

Space is one of those fields where almost every engineering decision becomes more challenging.

Communication can be delayed.

Resources are limited.

Environments are extreme.

Failures can be incredibly expensive.

And spacecraft often need to operate with significant levels of autonomy.

That means future space systems won't just need better hardware.

They'll need better intelligence.

Imagine systems capable of:

• detecting dangerous orbital situations,
• reasoning about uncertain trajectories,
• assisting spacecraft maneuver decisions,
• allowing lunar robots to navigate when visibility disappears,
• detecting anomalies before they become failures,
• or helping autonomous spacecraft make decisions without constant human intervention.

These aren't simply AI problems.

They're AI + physics + engineering problems.

That's the combination I find exciting.

One of My Projects: ORBITGUARD

One project that pushed me further into this intersection is ORBITGUARD.

I started exploring the problem of space debris and satellite collision risk.

There are thousands of objects orbiting Earth, and understanding whether two objects could approach dangerously close requires much more than looking at their positions on a screen.

While developing the project, I started learning concepts such as:

• Two-Line Elements (TLE)
• SGP4 orbital propagation
• Time of Closest Approach (TCA)
• Miss distance
• Relative velocity
• Orbital mechanics
• Collision-risk analysis

I then began exploring how computational and AI techniques could complement the physics layer through concepts such as anomaly detection and uncertainty-aware analysis.

One lesson became very clear:

AI shouldn't replace physics in safety-critical engineering systems.

Instead, AI can search, detect patterns and assist decisions while physics provides an independent layer of verification.

That idea has heavily influenced how I think about intelligent engineering systems.

Beyond ORBITGUARD

I'm also interested in problems where traditional sensing may fail.

One direction I'm exploring involves Non-Line-of-Sight lunar rover docking.

Imagine a lunar rover trying to return to a docking or charging station.

Normally, cameras or other optical systems could help guide it.

But what happens when visibility becomes unreliable?

What if darkness, terrain or lunar dust interferes with the line of sight?

This leads to an interesting engineering question:

Can multiple sensing methods and intelligent sensor fusion help a rover dock when vision alone isn't enough?

It's still something I'm exploring, but these are exactly the kinds of questions I want to investigate.

And Then There's Quantum Computing

Another field I'm beginning to explore is Quantum Computing.

I'm particularly interested in its possible intersection with machine learning and explainable AI.

Quantum machine learning is still a developing research area, which makes it both challenging and exciting.

For me, this isn't about attaching the word "quantum" to an AI project.

I want to understand the fundamentals first:

What problems could quantum approaches genuinely help with?

Where would classical machine learning still be better?

Can quantum models become interpretable?

Those are questions I hope to explore through future projects and research.

Why I'm Starting This Blog

It's easy to show a finished project on GitHub.

What's harder to see is everything that happened before the final commit.

The failed experiments.

The bugs.

The concepts that initially made no sense.

The design decisions.

The approaches that didn't work.

And the moment when something finally did.

That's what I want this blog to capture.

I'll be writing about things such as:

Space Technology

Orbital mechanics, spacecraft systems, autonomous space systems and projects I'm building.

AI/ML

Models, experiments, datasets, mistakes and what I'm learning while developing intelligent systems.

Programming

Python, competitive programming, algorithms and software engineering.

Quantum Computing

My journey from fundamentals toward quantum machine learning and research.

Research

Interesting questions, papers I'm studying, experiments and research projects.

Hackathons

How projects evolve under tight deadlines, what goes wrong and what I learn from building with a team.

Where I Want This Journey to Go

I don't know exactly what I'll be building several years from now.

And I actually like that.

Right now, my goal is simpler:

Learn deeply. Build constantly. Ask difficult questions. Document the process.

I want to become someone who understands both the algorithms running inside a system and the engineering principles governing the world that system operates in.

Because some of the most interesting problems of the future won't belong to a single discipline.

They'll exist at the intersection of many.

For me, one of those intersections is:

AI × Space × Engineering × Research.

And this blog is where I'm going to document the journey.

Thanks for reading.

This is only the beginning.