The Ferret - Tunnel Exploring Robot (#1)

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

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:

1. Define constraints 
2. Explore design options 
3. Clarify tradeoffs 
4. 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

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!

Robots in the Wild: Deep Trekker - ROV

Spotted the other day: a robot in the Wild!  A field crew from H2O Drones B.V. running a well-used Deep Trekker REVOLUTION ROV through an underground passage carrying the north branch of the Düssel river (Nördliche Düssel).

H2O Drones - Deep Trekker ROV - exploring the Düssel

I was not expecting to see a robot crew working out of the back of their van on my walk. Bright yellow tether cables were looped across the pavement, a scratched-up robot perched beside a manhole, and the crew was preparing to send it on a mission into a dark, flooded tunnel under the city. This was no prototype lab machine, not a trade-show robot, but a working robot covered in the scratches, scuffs, residue, and improvised mounts that come from doing real inspection work.

The mission was to scan and search roughly 500 meters of underground river passage. Like many European cities, Düsseldorf has built over the majority of the waterways that feed into the larger rivers. Today the Düssel was running low after the recent heat dome and lack of rainfall, which likely made access easier, but the tunnel still looked like the kind of confined, dangerous, flooded, low-visibility environment where sending a robot is much better than sending a person.

Deep Trekker - Revolution ROV - well used!

The ROV itself looked heavily used, this was a working robot in all respects. The base platform was a Deep Trekker REVOLUTION, I was told - the logos had all been worn off! The ROV already has a primary lighting, camera, and sonar. This robot was fitted with additional equipment bolted to the top frame. The extra hardware looked like auxiliary inspection gear, a large flood light and a camera, or other optical sensing equipment for navigating and documenting the tunnel. Perhaps you can identify the additional sensor, leave a comment.

Additional Sensors bolted to the top of the ROV

This was a real working robot, that is what made it such a good “robots in the wild” sighting. The robot was not a camera drone taking pictures of the city. It was doing infrastructure work: going where people do not easily fit, doing real inspection work. And to me, the exciting part of exploring a hidden underground space - A small robot, a long cable, a dark river tunnel, and 500 meters of the unknown ahead. Very cool to stumble across.

P.S. The crew from H2O Drones were professional and very friendly. They obviously loved their job. When asked what was the most interesting thing they experienced doing inspections, they said "finding a beaver!" 

Cybernetic AI Agents for Robots - Stafford Beer's VSM (#2)

Now let’s talk about Cybernetics

The word cyber has been stretched in every possible sci-fi direction. It gives us cybermen, cyborgs, cyberpunk, and every variety of futuristic machine mythology. In popular culture, it usually means something digital, machine-enhanced, or vaguely threatening. Cybernetics is not a science-fiction aesthetic, it is a scientific and intellectual discipline concerned with control and communication in systems, e.g. biological, mechanical, or social.

Cybernetics began with Norbert Wiener, who established it as a formal field around control, communication, and feedback. From there, the field broadened through the work of thinkers such as W. Ross Ashby, Roger Conant, Stafford Beer, William T. Powers, and, in robotics, Rodney Brooks. Each contributed a different piece to the question of how systems persist, adapt, and remain effective in a changing environment.

Stafford Beer, the management theorist and philosopher, is especially important here. Beer’s major contribution was not about machine control, but the application of cybernetics to management and organizations. He was concerned with how complex organizations—companies, factories, governments, and institutions—could remain functional, adaptive, and coherent under changing conditions. His work developed into what is commonly known as the Viable System Model (VSM).

Beer’s key question was what makes a system viable. In this context, viability means more than simply functional or productive. It means the ability of a system to continue functioning as itself: maintaining coherence, adapting to change, absorbing disruption, and preserving its purpose despite internal complexity and external pressures. For organizations, this is essential. An organization that cannot coordinate itself, regulate itself, adapt to its environment, and remain aligned with its purpose will eventually drift, fracture, or fail. Beer’s model was an attempt to describe the minimum functions required for a system to remain viable over time.

In Beer’s model, viability depends on a set of interacting systems:

• System 1 – Operations -- This is the level where the actual work gets done. These are the operational units carrying out the primary activities of the system.
• System 2 – Coordination -- This system reduces conflict and oscillation between operational units. It helps ensure that the parts of the system do not work against one another and can function together in a stable way.
• System 3 – Control / Internal Regulation -- This level provides internal oversight of the operational systems. It allocates resources, enforces constraints, and ensures that the organization is functioning as a coherent whole.
• System 3* – Audit -- This is the auditing or monitoring function. It checks what is actually happening at the operational level, rather than relying only on reported summaries. It provides a direct channel for verification.
• System 4 – Intelligence / Environmental Scanning -- This system looks outward and forward. It monitors the environment, detects change, evaluates threats and opportunities, and considers how the system must adapt in order to remain viable.
• System 5 – Policy / Identity / Purpose -- This is the highest-order level. It defines the identity of the system, its governing policy, and its reason for existing. It is what ultimately anchors the rest of the organization.

The importance of this structure of divides responsibilities is that it establishes a relationship between immediate action, internal regulation, adaptation, and purpose. Lower systems can perform their responsibilities while being guided by the overall identity and purpose of the system. A viable organization can change its operations, coordination, and even its strategic posture, but it does so without losing itself.

Autonomy, resilience and Agents

That point connects to a broader cybernetic theme. An Agent is not meaningfully autonomous simply because it can perform tasks or optimize outputs on its own. What matters is whether it can regulate itself, adapt to change, and remain coherent while pursuing its purpose. In all cases, the environment will change, and that will inevitably require internal adaptation; cybernetics is best applied here to ensure that the adaptation keeps the system aligned to its purpose.

This is why cybernetics matters for the future of AI agents. Agents have become more capable, they are able to modify workflows, generate code, restructure processes, and adapt behavior. The problem is no longer repeatable execution. The real problem is how these types of adaptations remain organized, how they remain bounded, how they avoid drift, and how they continue to serve the original purpose of the system.

Application to Autonomous Robotics

This is where my interest is heading. My goal is a truly autonomous robot: a viable system that can operate, regulate itself, maintain its own health, adapt to change, and evolve without losing its purpose. AI agent technology makes this kind of autonomy far more achievable. But capability alone is not enough. For autonomy to be complete, it needs governance. It needs internal regulation. It needs a structure that allows adaptation without collapse, change without drift, and evolution without loss of identity. That is why Beer’s cybernetic model matters.

Next, I want to look at a modern adaptation of these ideas, and how to build a much better Totally Not Evil Robot Army.

Cybernetic AI Agents for Robots (#1)

 "The Future's So Bright, I Gotta Wear Shades"

[editor's note:  I have returned after a hiatus due to life, jobs, and other distractions (Factorio!! this is why we don't have my robots everywhere!). AI is everywhere now, but for my blog/writings I will still struggle to produce for you my thought content. I may use AI to shape it up and improve the clarity. But the thoughts written are mine.]

A new world of AI Agents

A great deal has changed in the world since 2025. AI has advanced significantly, and agent technology has taken a major step forward. It is becoming evident that AI systems are moving toward a higher level of intelligent capability, with important implications for robotics.

The core concept is straightforward. Instead of robots being purely deterministic systems—fixed sets of code that either follow predefined instructions or simply receive commands about what to do next—a robot could have the ability to analyze how to achieve a given result on its own. In other words, the system would be given a purpose or goal, but it would be allowed to determine its own approach. It could assess options, simulate possibilities, decide what to do, and then take action accordingly.

A key part of this shift is that AI agents today are beginning to show the ability to write quality code, rewrite code, adjust code, and modify their operating processes to better fit the goals they are trying to achieve. This is more than just using a Agent Planner with a set of Skills. It is the ability to build the Skills needed to accomplish an objective.

Oddly enough, much of this is still very linguistic and centered around the LLM. Large language models do not directly drive real-time operations well, but they do have the ability to write the code that can drive those real-time operations. That is where I think this begins to apply to robotics.

You could imagine a robotic system that has the ability to adapt to its sensors and reconfigure how those sensors are used. It could adjust how motion is implemented, or even how control systems are tuned, in order to achieve the result it is aiming for. This higher-level function would have to operate on a completely different layer than the real-time operating system, motion control, or sensor loop.

That difference in timescale is important. This higher level of operation would run on a different sequence of events than real-time sensing, actuation, and low-level control. But that does not mean it cannot exist. It simply means that the architecture must separate high-level adaptive reasoning from low-level real-time execution.

Such a system would likely require some connection to a large language model. Today, we have powerful models in the cloud, but AI and LLM technologies are rapidly being compressed into smaller footprints. Capabilities are increasingly moving toward deployment on smaller devices, to the point where something like this could eventually be embedded directly within a robotic platform.

This may also change how humans interact with machines. This is how we may begin to talk to robots. It is how you might talk to your car and ask what is wrong with it. It is how you might tell your dishwasher what you want it to do and have it respond conversationally, including explaining the likely impact of your command.

But that is only the first step. Talking to machines is one thing. The deeper shift is that the robot itself could have the ability to rewrite the code it uses to perform the functions it needs to do. That is a much more significant capability.

A robot like this could evolve over time. It could even reach the point where it can work with modular sensors or drive mechanisms, and when presented with new hardware, it could update its own operating parameters and implement new functionality on its own. In that sense, it would not merely execute control. It would adapt its own control.

And that leads naturally into Cybernetics...

PolyMap & MinOne - MinOne gets Log-Odds (#5)

Improving Minone and PolyMap with Log-Odds

I have recently upgraded Minone and the PolyMap mapping system, focusing particularly on enhancing our use of a Single Ultrasonic Sensor. While the sensor hardware remains unchanged, introducing the concept of "log-odds" has significantly reduced the impact of noise and false readings, making our maps clearer and more accurate.

What are Log-Odds? (Explained Simply)

Imagine you're guessing whether it will rain today. You might say there's a 50/50 chance—that's "even odds." Now, suppose new clouds roll in, and your chances become "likely" rain, maybe 75%. Each time you get new information, your confidence about rain happening or not happening changes.

Log-odds is just a special way of keeping track of these changing guesses. It turns probability—the chance something is true—into a number that's easier to update quickly. Positive log-odds numbers mean "more likely," negative log-odds numbers mean "less likely," and zero means completely uncertain.

Occupancy Grids - Binary vs Log-Odds

Applying Log-Odds in PolyMap's Occupancy Grids

In mapping, especially with sensors like the ultrasonic sensor used in Minone, each measurement tells us something about whether space is empty or occupied. However, sensors aren't perfect; sometimes they say there's something there when there isn't (false positive) or fail to detect something that really is there (false negative).

Using log-odds, The system doesn't just accept each measurement blindly. Instead, it builds confidence over multiple readings. Every time the ultrasonic sensor scans an area:

• A positive reading increases the log-odds, suggesting the area might be occupied.

• A negative reading decreases the log-odds, suggesting the area is likely empty.

Over time, genuine obstacles build a strong positive score, clearly marking them as occupied on our map. Meanwhile, random noise, causing occasional false readings, doesn't build enough consistent evidence, so these points eventually fade away, staying neutral or negative.

This approach makes the PolyMap occupancy grids more reliable and accurate, greatly improving how robots navigate and interact with their surroundings.

A Deeper Look into the Math of Log-Odds

Log-odds translate probabilities from their natural scale (0 to 1) into a 'logarithmic space,' which makes it easier and computationally more efficient to combine multiple pieces of evidence. In probability space, values close to 0 or 1 become increasingly difficult to update because small incremental changes can have disproportionate effects. By shifting to log-space, updating becomes straightforward additions and subtractions, maintaining precision across multiple sensor updates.

When updating log-odds:

• Positive evidence: If our sensor indicates an obstacle, we add a fixed positive log-odds increment, quickly shifting the belief towards occupied.

• Negative evidence: If the sensor suggests no obstacle, we subtract a smaller fixed increment, slowly shifting back towards uncertainty or free space.

To prevent numerical instability, clamps are applied to the log-odds values. These clamps set upper and lower bounds, ensuring values remain within practical limits, preventing overruns that could result in overly confident (extreme) occupancy or vacancy assertions.

This additive and controlled adjustment property allows for quick, stable, and efficient updating, even with multiple sensor readings, greatly enhancing computational efficiency and clarity in interpreting sensor data.

Practical Results from PolyMap

Since implementing log-odds in PolyMap, there are substantial improvements:

• Reduction of False Negatives: Previously, true obstacles were sometimes overlooked due to sensor noise and rapid changes in sensor readings. By quickly reinforcing log-odds with positive detections and slowly decreasing with negative readings, our system now reliably maintains the presence of real obstacles, significantly reducing false negatives.

• Clearer Obstacle Identification: True obstacles are now distinctly recognized after several confirmations, making navigation decisions safer and more confident.

These practical outcomes directly enhance robot efficiency, reducing navigation errors and improving path planning capabilities.

Future Improvements and Considerations

Moving forward, I will be exploring additional enhancements to further refine occupancy grid accuracy:

A key challenge is tackling the ultrasonic sensor's tendency to miss obstacles when pulses reflect at oblique angles, effectively making these obstacles invisible. This issue arises because the sensor’s sound pulse can deflect away, failing to return the echo needed for detection. To address this, I am considering several options:

• Sensor Fusion: Integrating data from multiple sensor types, such as infrared, alongside our ultrasonic sensor to build an even more accurate picture.

• Multi-Angle Sensors: Using multiple ultrasonic sensors at different angles to ensure obstacles are detected from various perspectives.

• Adaptive Orientation: Dynamically adjusting the sensor's orientation or positioning to better capture reflections from challenging angles.

• Enhanced Signal Processing: Implementing advanced signal analysis techniques to better distinguish weak echoes or indirect reflections.

I believe these improvements will further strengthen PolyMap's reliability, making the robot even smarter and more autonomous.