1 Robot Motion and Perception (ch. 5, 6) These two chapters discuss how one obtains the motion model...

1

Robot Motion and Perception(ch. 5, 6)

• These two chapters discuss how one obtains the motion model and measurement model mentioned before.

• They are needed for implementing Bayes filter algorithm,

• And the Bayes filter algorithm is the mother of all other algorithms in the book….

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Robot Motion and Perception(ch. 5, 6)

•What’s your thought on motion model and measurement model?

p(x’|x, u), p(z|x)

•How would you get them? Supplied by somebody else?

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Robot Motion and Perception(ch. 5, 6)

•Kinematics is the calculus describing the effect of control actions on the configuration of a robot.

•The configuration of a robot….• Euler Angle, roll, pitch, yaw,…• http://www.nasm.si.edu/exhibitions/gal109/NEWHTF/HTF541B.HTM

•See figure 5.1.

•The pose of a robot (location+bearing).

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Probabilistic Kinematics

Key question:

We may know where our robot is supposed to be, but in reality it might be somewhere else…

VR(t)

VL (t)

starting position

supposed final pose

x

y

lots of possibilities for the actual final pose

What should we do?

5

running around in squares

• Create a program that will run your robot in a square (~2m to a side), pausing after each side before turning and proceeding.

1

2

3

4

• For 10 runs, collect both the odometric estimates of where the robot thinks it is and where the robot actually is after each side. • You should end up with two sets of 30 angle measurements and 40 length measurements: one set from odometry and one from “ground-truth.”• Find the mean and the standard deviation of the differences between odometry and ground truth for the angles and for the lengths – this is the robot’s motion uncertainty model.

start and “end”

This will provide a probabilistic kinematic model.

MODEL the error in order to reason about it!

6SA-1SA-1

Probabilistic Robotics

Probabilistic Motion Models

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Robot Motion

• Robot motion is inherently uncertain.

• How can we model this uncertainty?

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Dynamic Bayesian Network for Controls, States, and Sensations

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Probabilistic Motion Models

•To implement the Bayes Filter, we need the transition model p(x | x’, u).

•The term p(x | x’, u) specifies a posterior probability, that action u carries the robot from x’ to x.

• In this section we will specify, how p(x | x’, u) can be modeled based on the motion equations.

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Coordinate Systems

• In general the configuration of a robot can be described by six parameters.

• Three-dimensional cartesian coordinates plus three Euler angles pitch, roll, and tilt.

• Throughout this section, we consider robots operating on a planar surface.

• The state space of such systems is three-dimensional (x,y,).

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Typical Motion Models

• In practice, one often finds two types of motion models:• Odometry-based

• Velocity-based (dead reckoning)

• Odometry-based models are used when systems are equipped with wheel encoders.

• Velocity-based models have to be applied when no wheel encoders are given.

• They calculate the new pose based on the velocities and the time elapsed.

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Typical Motion Models

• In practice, Odometry models tend to be more accurate than Velocity models.

•Odometry-based models are suitable for estimation, but not for planning.

•Velocity-based models are used for probabilistic motion planning.

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Reasons for Motion Errors

bump

ideal case different wheeldiameters

carpetand many more …

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Odometry Model—Set up

tx

1tx•At time t, the correct pose of the robot is modeled by the random variable .

•The robot needs to estimate this pose.

•In the time interval (t-1, t], the robot advances from a pose to pose .

•The odometry reports back a related advance from to .

•The bar here indicates that there are measurements.

tx

Tt yxx ),,(1

Tt yxx )',','(1

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Odometry Model—Set up

•The key insight here is to use the difference between and as a good estimator for the difference of the true pose and .

•The motion is given by

1tx tx1tx tx

tu

t

tt x

xu 1

•The motion is transformed into a sequence of three steps: a rotation, a straight line motion (translation), and another rotation.

•See Figure 5.7

tu

transrotrott

tt x

xu ,, 21

1

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Odometry Model

22 )'()'( yyxxtrans

)','(atan21 xxyyrot

12 ' rotrot

• Robot moves from to .

• Odometry information .

,, yx ',',' yx

transrotrotu ,, 21

trans1rot

2rot

,, yx

',',' yx

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The atan2 Function

• Extends the inverse tangent and correctly copes with the signs of x and y.

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Noise Model for Odometry

•The measured motion is given by the true motion corrupted with noise.

||||11 211

ˆtransrotrotrot

||||22 221

ˆtransrotrotrot

|||| 2143

ˆrotrottranstranstrans

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Typical Distributions for Probabilistic Motion Models

2

2

22

1

22

1)(

x

ex

2

2

2

6

||6

6|x|if0)(2

xx

Normal distribution Triangular distribution

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Calculating the Probability (zero-centered)

• For a normal distribution

• For a triangular distribution

1. Algorithm prob_normal_distribution(a,b):

2. return

1. Algorithm prob_triangular_distribution(a,b):

2. return

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Calculating the Posterior Given x, x’, and u

22 )'()'( yyxxtrans )','(atan21 xxyyrot

12 ' rotrot 22 )'()'(ˆ yyxxtrans )','(atan2ˆ

1 xxyyrot

12ˆ'ˆrotrot

)ˆ|ˆ|,ˆ(prob trans21rot11rot1rot1 p|))ˆ||ˆ(|ˆ,ˆ(prob rot2rot14trans3transtrans2 p

)ˆ|ˆ|,ˆ(prob trans22rot12rot2rot3 p

1. Algorithm motion_model_odometry(x,x’,u)

2.

3.

4.

5.

6.

7.

8.

9.

10.

11. return p1 · p2 · p3

odometry values (u)

values of interest (x,x’)

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Application

• Repeated application of the sensor model for short movements.

• Typical banana-shaped distributions obtained for 2d-projection of 3d posterior.

• See Figure 5.8.

x’u

p(x|u,x’)

u

x’

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Sample-based Density Representation

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Sample-based Density Representation

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How to Sample from Normal or Triangular Distributions?

• Sampling from a normal distribution

• Sampling from a triangular distribution

1. Algorithm sample_normal_distribution(b):

2. return

1. Algorithm sample_triangular_distribution(b):

2. return

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Normally Distributed Samples

106 samples

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For Triangular Distribution

103 samples 104 samples

106 samples105 samples

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Sample Odometry Motion Model

1. Algorithm sample_motion_model(u, x):

1.

2.

3.

4.

5.

6.

7. Return

)||sample(ˆ21111 transrotrotrot

|))||(|sample(ˆ2143 rotrottranstranstrans

)||sample(ˆ22122 transrotrotrot

)ˆcos(ˆ' 1rottransxx )ˆsin(ˆ' 1rottransyy

21ˆˆ' rotrot

',',' yx

,,,,, 21 yxxu transrotrot

sample_normal_distribution

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Sampling from Our Motion Model

Start

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Examples (Odometry-Based)

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Velocity-Based Model

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Equation for the Velocity Model

Center of circle:

with

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Posterior Probability for Velocity Model

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Sampling from Velocity Model

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Examples (velocity based)

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Map-Consistent Motion Model

)',|( xuxp ),',|( mxuxp

)',|()|(),',|( xuxpmxpmxuxp Approximation:

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Summary

• We discussed motion models for odometry-based and velocity-based systems

• We discussed ways to calculate the posterior probability p(x| x’, u).

• We also described how to sample from p(x| x’, u).

• Typically the calculations are done in fixed time intervals t.

• In practice, the parameters of the models have to be learned.

• We also discussed an extended motion model that takes the map into account.