AWMR Project Presentation (Prathmesh)

EE 5380 AUTONOMOUS WHEELED MOBILE ROBOTS

SEMESTER PROJECTBy

Prathmesh.R.Kumbhare ([email protected])

and Kaustubh.A.Deshpande

([email protected])

PROJECT COMPONENTS

1) Platform :- 4-Wheel WMR, Ackermann Steered, Rear Wheel Drive

2) Dynamic Controller :- Neural Network Controller (On-line Weight Tuning)

3) Sensors :- i) RADAR and ii) SONAR

4) Localization Algorithm :- Markov Localization

5) Navigation Algorithm :- Vector Field Histogram (VHF)

4-Wheel WMR, Ackermann Steered, Rear Wheel Drive

• Geometry of the mobile robot is as shown below –

Kinematics of the Mobile Robot

• What are kinematics?

• Why are they needed?

• There are two nonholonomic constraints present for this robot system viz.-1) Rolling Without Slipping 2) No Sliding

• These constraints are used to derive the kinematic equations of the robot.

Kinematics of the Mobile Robot• Derivation:



Dynamics of the Mobile Robot• What are dynamics ?• Why are they needed ?• There are two method to derive the dynamics

a) Newton-Euler method b) Lagrange method• Similar to kinematics, dynamics is divided into

i) Direct Dynamics ii) Inverse Dynamics

Dynamics of the Mobile Robot• Derivation:

Dynamics of the Mobile Robot





Dynamic controller

Dynamic controller

Dynamic controller• Approach to design controller:

• 1. Function f(x) contains all the mobile robot parameters like mass, moment of inertia, friction coefficient, etc. These parameters are often partially known.

• 2. This function is estimated using neural network mentioned earlier.

• 3. A torque like control is computed from this estimated non-linear robot function.

Dynamic controller• Advantage:

• 1. Most common problems encountered in navigation, namely trajectory following, path following & point stabilization can be overcome ( NN weight tuning laws are crucial in making velocity tracking and position error UUB)

Sensors• These are two sensors which will be

implemented in project:

• 1. Sonar

• 2. Radar

Sonar sensor• Ultrasonic refers to very high frequency sound(it

is higher than human hearing range).

• Sonar(Sound Navigation & Ranging) is an application of ultrasonic sound which is used to navigate and detect obstacles.

• Sonar sensors work by – emitting a short burst of ultrasonic sound (often 40

kHz) – sensing reflected signals (if any)– computing object distance by using the elapsed time

RADAR• Radar stands for Radio Detection and Ranging and is

based on the use of radio waves. • How does Radar work ?

• The radar uses 3 pieces of information to determine the location of the target-

a) The azimuth angle, the angle of the radar beam with respect to the north.b) The elevation angle, the angle of the radar beam with respect to the ground. c) The distance (D) from radar to the target.

• Distance is measured by the time it takes for the EM pulse to make a round trip from the radar to the target. If the time taken is ‘t’ then 2D = c*t or

D= (c*t)/2.

Markov Localization• Markov localization uses an discrete representation for the probability of all

positions in the robot’s configuration space.• It uses a fixed decomposition grid in the configuration space, discretizing

each degree of freedom.(x, y, Θ)• For each location xi = (x, y, Θ) in the configuration space, we determine the

probability of the robot being in that space.• In Markov localization, we assume that the Markov property holds true. It

says that –

“ A stochastic process satisfies the Markov property if it is conditional only on the present state of the system and its future and past are independent.”

• Updating the belief state,

i) We use the theorem of total probability for action updates.

p(x) = ∫p(x|y)p(y)dy•

ii)For perception updates we use the Bayes theorem.

p(x|y) = p(y|x)p(x)/p(y)

Vector Field Histogram (VFH) • VFH is a real time motion planning algorithm proposed first by

Johan Borenstein and Yoram Koren 1991. • It utilizes a statistical representation of the robot’s environment

through the histogram grid and hence places a great emphasis on dealing with the uncertainty from the sensor and modelling errors.

• This method also takes into account the dynamics and shape of the robot and returns steering commands to specific to the platform.

• Using the histogram, all the paths through which the robots can pass are found and the one with the lowest cost function is selected.

• The cost function is calculated as

G = a.target_direction + b.wheel_orientation+c.previous_direction

References• 1) Spyros Tzafestas, Introduction to Mobile Robot

Control, Elsevier, 2014• 2) Control of a nonholonomic mobile robot :

Backstepping Kinematics into Dynamics, R.Fierro and F.L.Lewis, Journal of Robotic Systems, 1996.

• 3) The Vector Field Histogram-Fast Obstacle Avoidance for Mobile Robots, J. Borenstein, Y. Koren, IEEE Journal Robotics and Automation, Vol 7, No 3 , June 1991

THANK YOU

Documents

AWMR Project Presentation (Prathmesh)