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| TNERA - The Ferret Robot - AI Concept Art |
Introduction
I always had a dream in the back of my head about having a robot to go into tiny spaces that I could not. To go down the proverbial rabbit hole and see what is there. This is my goal, to build exploring robots. Even ones that could be recast as pipe-inspection robots.
Sounded to me like it would be a good weekend project, it also sounded like a good 'new' project that I could test out the capabiltities of using the current AI tools to help build the components. It would be a good test to see how they have improved in the last year.
So, down the rabbit hole we go! Chasing the AI white rabbit!
Like it or not, We are living in a moment in time in which AI has created a time of software abundance. It is now far easier for individuals to create complex new software with ease. There’s a growing claim, a belief, that "Now anyone, can build anything". Many software Makers are doing just that: they are going back to previous projects that were to onerous to build in the past, and now building them.
Can this be true for robotics projects as well?
Robotics has always been one of those domains that resists simplification. It is a heavy concentration of software, firmware, electronics, and mechanical design, all interacting in ways that tend to create complexity instead of removing it. Historically, that complexity has been a barrier for automated tools, it also makes it fantastically more interestings.
So this project—The Ferret—is my attempt to explore whether modern AI tools can meaningfully compress that complexity.
The Idea: A Burrow-Exploring Robot
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| Abandoned Burrow Hole - to be explored |
I’ve had this idea for years: a small robot that can crawl into spaces people can’t—burrows, holes, confined environments—and explore them remotely.
To do this, I need something narrow. Cylindrical. Tracked.
A robot that can push forward into an unknown space with a camera and light, acting as a remote presence where a human simply can’t go.
The Core Challenge: Does current AI help here?
Any robotics project sits at the intersection of three domains:
- Firmware - software that runs on the device
- Electronics - how components are wired and powered
- Mechanical design - what the robot physically exists as
AI is clearly strong in firmware and all software development. This part has already become trivial with early 2026 LLMs. Write a good prompt on what you want the firmware to do, and the AI agents can write it for you in minutes.
But how can AI be used to Generate usable 3D printable CAD? used to help design mechanical systems? or provide meaningful assistance with circuitry? This is the real test.
Starting Mechanical Design
The process started not in CAD, but in conversation. Before building anything, I worked with an AI session to:
- Define constraints
- Explore design options
- Clarify tradeoffs
- Generate prompts that would later drive development
This turned out to be a useful pattern: design through dialogue first, implementation second.
OpenSCAD and the Nose Cone
I use OpenSCAD for CAD work. It is openSource, and completely codified, which makes it easy for an LLM to write the design in.
Historically, AI has been… really bad at it. Almost unusable.
So I started manually, designing the front of the robot—a nose cone. I knew what I wanted geometrically, but not what to call it. So I asked Gemini Flash. It identified the shape as an elongated ellipsoid, common in aerodynamic designs, and even generated OpenSCAD code for it. I pasted it in.
It worked immediately.
That was the first signal that something had changed.
Building the Tracks
With that success, I quickly moved over to the design of the caterpillar tracks. This wasn’t my first time building tracked systems (see the Wild Weasel). I had a good mental model of how it should work. The AI suggested I use Linked track system, where there are two distinct parts like a bicycle chain or chain on a chainsaw. There is some benefit of the cog locking of that method. However, I would prefer a universal (continuous) track system based on preference and past experience. The continuous track uses a single universal track tread that is much easier to print in mass.
The Ferret Robot - Single Track Piece CAD design
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| The Ferret Robot - Track Pieces in line CAD design |
I spent about an hour designing the first track segment manually. It worked well. a base tread, with a hinge that I could insert small metal rods into. Then, I asked GPT to generate a similar design based on my comments. The initial CAD code output was not very useful. However, I uploaded my design as a rough guide not fully spec'd out, and the AI was able to match and complete the track accurately. The AI was not perfect 'from scratch' like the nose cone, but it was highly effective when guided.
The Hard Part: Sprockets
Honestly, tracks are easy compared to sprockets. Sprockets are like gears, but they pull chains or track treads instead of meshing with other gears. The geometry of a sprocket is very different to a gear. To work correctly, sprockets require: precise tooth geometry, correct spacing, proper engagement with the track, provide alignment, and support tolerances. Designing a working sprocket is tedious and error-prone. Another good test of the AI!
The Ferret Robot - Sprocket CAD design
From above, I input the track design to GPT and provided a vague prompt to design a sprocket to fit the track. It did it in less than a minute. The AI generated the geometry, selected a tooth count, and calculated spacing and the included offsets. The design looked impressive, I added an axel and printed it to test.
Again, I found that the design didn’t work perfectly the first time—but it was close. It is not untypical in sprocket design to iterate through small variations to get the right fit and tolerances. This time, instead of manually editing the openSCAD code, I simply described the problem in plain language and let the AI adjust the design. It took five iterations to get a perfect fit. The only parameter I had to tune was offset, which is exactly what you’d expect in manual iteration anyway.
The Ferret Robot - Sprocket sizing iterations. Final on Right
Tensioner and frame on top with sprocket
Track tread length on bottom
The key difference: I never had to reason about sprocket geometry directly. The sprocket design was custom, not a library. the AI built the hub and teeth on its own. That’s a new skilled behavior to me, the AI has come a long way!
Initial mechanical design assessment
At this stage, I was pretty happy. The Nose Cone was pretty easy to "generate", the track design went better than expected. It took me signifcantly less interations to perfect (and it printed very nicely too). most of that design came from the Wild Weasel, the AI quickly parameterized it. And finally the sprocket was trivial, next to the typical effort to design and adjust. AI can now participate meaningfully in mechanical design—especially when guided.
Ferret Robot: Original motor idea
pre-V1: nose cone, ESP32-CAM, tracks, sprocket, battery, and motor driver.
So, I was feeling pretty happy. Happily building and happily ignoring some of the more obvious warnings I was getting feedback on from the AI... That is the next story!
