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Lesson10.markdown
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......@@ -112,17 +112,16 @@ Due to these poor results the original calibrated parameter values are used.
| Track width | 16.27 cm |
It seems like it is near impossible to calibrate the parameters so that the LEGO car is able to travel a given distance while performing a number of random turns. Multiple reason can cause this behavior. An obvious reason is the leJOS maximum precision of 2 %. So no matter how much calibration is performed some error will always exist. Other factors like the position of the back wheel could also be part of the explanation. If the back wheel is not aligned with the driving direction the car can be drawn out of course when pulling the back wheel into the correct direction.
## Position tracking by means of particle filters
## Position tracking by means of particle filters
### Estimating the noise factors
The tests to performed to estimate the noise factors was performed at low speeds, on a wooden table covered by a single sheet of paper. The low speed reduces the noise factors because the wheel stops faster, and thus closer to the intended angle, also the low speed reduces the risk the wheel loosing grip of the surface. The wooden table covered by paper also reduces the noise factors, because the surface is homogeneously smooth, without any irregularities which could introduce errors.
The tests to estimate the noise factors was performed at low speeds, on a wooden table covered by a single sheet of paper. The low speed reduces the noise factors because the wheel stops faster, and thus closer to the intended angle, also the low speed reduces the risk the wheel loosing grip of the surface. The wooden table covered by paper also reduces the noise factors, because the surface is homogeneously smooth, without any irregularities which could introduce errors.
The distance noise factor was determined by having the robot perform multiple forward travels of 500 mm. The average distance from the target was ~0.5 mm, thus the distance noise factor was estimated as: 0.5/500 = 0.001.
The distance noise factor was determined by having the robot perform multiple forward travels of 50 cm. The average distance from the target was ~0.5 mm, thus the distance noise factor was estimated as: 0.5/500 = 0.001.
The angle noise factor was determined by two tests, both performed multiple times. In the first test, the robot perform four 360 degrees rotation. The second test was similar, but reversed the direction of the 2nd and 4th rotation. For both tests, the average deviation was 0.5 degrees, hence the angle noise factor was estimated as: 0.5/360 = 0.00138.
The angle noise factor was determined by two tests, both performed multiple times. In the first test, the robot performed four 360 degrees rotation. The second test was similar, but reversed the direction of the 2nd and 4th rotation. For both tests, the average deviation was 0.5 degrees, hence the angle noise factor was estimated as: 0.5/360 = 0.00138.
### Test with `PilotMonitor` and `PilotRoute`
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