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Sensors for industrial mobile Robots Incremental sensors

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Presentation on theme: "Sensors for industrial mobile Robots Incremental sensors"— Presentation transcript:

1 Sensors for industrial mobile Robots Incremental sensors
Need more/better pictures

2 Magnetic encoders More reliable Less expensive
Less accurate than optical Less resolution (BUT can be used interpolators)

3 Magnetic encoders To sense direction

4 Interpolation to multiply resolution
A considerably higher resolution of angle φ is possible if we apply the interpolation process described in Figure. The graphic representation of sin(φ) versus cos(φ) produces a circle as a special Lissajous figure in which angle φ is immediately recognizable. The interpolator now compares the current angle value φ0  with predefined discrete angle positions and as a result generates the matching quadrature signals A and B.

5 A complete scheme with interpolator

6 Gyroscope – fiber optic

7 Gyroscope vibrating Coriolis force:

8 Example: Use of encoders + gyro
Steering encoder gyro Traction encoder … my first AGV prototype

9 The discrete form of the inertial-odometric navigation equations is as follows:
Use of encoders + gyro

10 Use of encoders + gyro

11 We have two separate estimations of the attitude so we have to combine them in a suitable (possibly optimum) way. To achieve this the two attitude increments are combined by a data-fusion algorithm in order to estimate the best guess for the two. The procedure is as follows: the two increments are estimated, the type of manoeuvre the vehicle is actually undergoing is estimated using the inertial-odometric data (table 2), the accuracy ratio of the output of the two navigation systems is estimated using table 1 the data fusion algorithm combines the two increments the output of the data-fusion algorithm is added to δk Use of encoders + gyro

12 Step 1 – attitude increments est.

13 Step 2 – manoeuvre estimation

14 Bayes Fusion (optimum in case of Gaussian white noise)
Step 3 – data fusion

15 Step 4 – accuracy ratio estimation
the accuracy ratio of the output of the two navigation systems is estimated using table 1 Step 4 – accuracy ratio estimation

16 Step 5 – add the increment
the output of the data-fusion algorithm is added to δk: Step 5 – add the increment


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