One day in 2009, I was lying in the grass near my dorm at the Twente University campus. I saw a DG-300 circle above me, which launched at the nearby airbase. Not long before I had learnt about the work of Alan Kay, who’s team at Xerox PARC invented about half of what we call “Personal Computing” today. Their way of thinking was new and exiting to me. One thought-provoking question that had stuck with me was: “What would be ridiculous not to have in 25 years?”
The dream
Watching the DG-300 circle in a thermal, I thought: “It would be ridiculous not to have a precise prediction of thermals in 25 years”. And so I started thinking….
What would gliding be like if we could prevent out-landings? If we could precisely predict the weather? Predict where thermals will form, when they will form and how fast we can climb in those thermals? This would be a game-changer. No more need for sustainer engines. No more need for late-night retrieves. That sounds pretty great….
Finding an exponential to make it happen
If we could use Moore’s law, this might not actually be that hard to achieve. In 25 years the amount of computing power on board of a glider will be a lot, about 100.000 times that of 2009. We can either use Moore’s Law to make the computers on board faster or to reduce it’s power consumption, or a bit of both. So even if batteries don’t scale exponentially in capacity over time, we can still use Moore’s Law to compute more for the same amount of energy.
Make it feasible
But 25 years is a long time. It probably doesn’t take 25 years to develop a solution. I decided that weather models, such as WRF, would probably be too slow. I decided something based on observations would probably work better than something based on predictions.
In 2009 cellular data wasn’t as ubiquitous as it is in 2025. My conclusion in 2009 was therefore: I should invest my time in Wireless Mesh Networking, so the gliders can communicate with each other without the need for cellular coverage. And that’s what I did: I spent my Master’s Thesis on Wireless Mesh Networking for gliders.
After finishing my thesis, I was not very content with the result. I started working, and the dream of live weather insights faded…. until I had access to the live tracking data from the SkyLines platform. When I looked at the SkyLines data around 2019, I saw that cellular coverage had dramatically improved. I could see that 97% of all GPS positions took less than 1 second to reach the SkyLines server. That means that the glider in question has a working cellular connection!
First try
This lead to my first attempts. First, I used XCSoar’s thermal detection algorithm to locate thermals. A clubmate of me flew with a moving map that showed thermals, and found two where I predicted them. Nice!
Then I started to look at faster cross country flights, and saw that a lot can be either won or lost during the gliding phase. I wrote a program that analyses OGN data and collects statistics of the entire world. I wrote another program that finds the fastest route from A to B given those statistics.
A friend did an 750km attempt in his ASW24, and I followed him live during the attempt. This is what I saw during that day:
Just after rounding the second turn-point I could predict his landing time… and I saw it slip. You can imagine that I was pretty over-the-moon by this result. I could find the most optimal route home in under 2 seconds on a Raspberry Pi 4! This might actually work….
However, later on when I tried to verify my algorithm formally, I unfortunately noticed critical errors in the algorithm and abandoned the project again.
This year
In the past years I didn’t know if what I wanted was feasible. I tried to remove some of the optimizations that violated correctness… but it didn’t work. I needed more than 16GB of memory to calculate a 50km route. It just wasn’t working.
I went back to studying the underlying algorithms, and talked to some friends about my approach and problems. I learnt some valuable things, but by the time I did I was focussing on Hobbes. Now that Hobbes has reached a conclusion, I learnt that prolonged attention can show great results after a few months.
So in 2025 I’m going to focus my attention on making the pathfinder work: make it correct, make it fast.