Hah, that reminds me of a robotics competition I took part in at school.
Robots were placed in an arena, and the objective was to gather QR-marked boxes, and bring them back "home".
There were also QR codes placed at known locations around the arena, and the intention was that you could use them for navigation. However, the camera systems were pretty flakey (especially under unpredictable lighting conditions), so we wanted to avoid using them as much as possible. So, we put rotary encoders on our wheels, and integrated the readings to calculate a "home vector" for the return journey, just like you described.
At the time, I wasn't aware that this was an ant-inspired technique - but it was very effective, and we won the competition. Thanks, ants!
I’m sure the ants have some kind of error correction, and even if it is a one-off robot, you can apply a personal error correction, but it will be specific to that robot. There is no “general” amount of error because every motor/encoder/circuit will provide different levels of error.
You can't just "error correct" accumulated random errors. The only way to be accurate is to never lose precision in the first place (which makes the ants especially impressive).
Robots were placed in an arena, and the objective was to gather QR-marked boxes, and bring them back "home".
There were also QR codes placed at known locations around the arena, and the intention was that you could use them for navigation. However, the camera systems were pretty flakey (especially under unpredictable lighting conditions), so we wanted to avoid using them as much as possible. So, we put rotary encoders on our wheels, and integrated the readings to calculate a "home vector" for the return journey, just like you described.
At the time, I wasn't aware that this was an ant-inspired technique - but it was very effective, and we won the competition. Thanks, ants!