1. Cover Option2

2. MODULE 5 SECTION 2
Robotics
Section Beginning (Dark Color Option )

3. Robotics
Introduction
Robot Hardware
Robotic Perception
Planning to Move
Dynamics and Control
Robotic Software
Applications

4. Introduction
Robots are equipped with effectors.
Effectors Actuators
Assert a force on Communicates a
the environment command to an effector

5. Types of Robots
Manipulators
Anchored to the workplace.
Common industrial robots.
2. Mobile Robots
Move using wheels, legs, etc.
Examples: delivering food in hospitals,
autonomous navigation, surveillance, etc.

7. Types of Robots
Hybrid (mobile with manipulators)
Examples: humanoid robot
(physical design mimics human torso)
Made by Honda Corp. in Japan.

9. Robotics
Introduction
Robot Hardware
Robotic Perception
Planning to Move
Dynamics and Control
Robotic Software
Applications

10. Robot Hardware
Sensors:
Passive sensors.
True observers such as cameras.
b. Active sensors
Send energy into the environment,
like sonars.

13. Sensors
Examples of sensors:
Tactile sensors (whiskers, bump panels)
Global Positioning System
Imaging sensors
Odometry (distance travelled)

14. Effectors
Characterized by the degrees of freedom DF.
DF counts one for each independent direction
of movement.
6 degrees of freedom are required to place
an object at a particular orientation.

16. Other Types of Effectors
Unlike wheels, legs can handle tough terrains,
but they are slow on flat surfaces.
Devices vary from one leg to dozens of legs.
Robots can be
dynamically stable
dynamically unstable

18. Sources of Power
The electric motor is the most popular source
But you may also see:
Pneumatic actuation using compressed
gas.
Hydraulic actuation using pressurized
fluids.

19. Robotics
Introduction
Robot Hardware
Robotic Perception
Planning to Move
Dynamics and Control
Robotic Software
Applications

20. Robotic Perception
Can be illustrated using a
Bayesian Belief Network.
It can be defined as a temporal inference
from sequences of actions and measurements.

22. Other Robotic Tasks
Localization
Mapping
Perception of
Temperature
Odors
Acoustic signals
Quantities can be estimated probabilistically.

23. Robotics
Introduction
Robot Hardware
Robotic Perception
Planning to Move
Dynamics and Control
Robotic Software
Applications

24. Planning to Move
Types of motion:
Point-to-Point.
Deliver robot to target location.
Compliant motion.
Move while in contact to an obstacle
(robot pushing a box).

25. Configuration Space
Working Space:
Spatial coordinates.
Problem: not all coordinates are attainable
Configuration Space:
Represent robot joints.
With two joints we need two angles
(e.g., for shoulder and elbow).

27. Configuration Space
The space can be decomposed into
two subspaces:
Free space. Space of attainable
configurations.
b. Occupied Space. Space of unattainable

31. Methods to Move
Cell Decomposition.
Decompose the free space into a number
of contiguous regions, called cells.
The problem is a discrete graph search problem.

33. Methods to Move
Cell Decomposition.
Disadvantages:
Limited to low-dimensional configurations.
Cells may be “mixed”.
(solution: make cells more granular).
Path may get too close to obstacles.
(solution: use a potential field).

34. Potential Field
A function defined over state space.
Value grows with distance to closest obstacle.
Tradeoff:
Minimize path length to goal while staying
away from obstacles.

37. Skeletonization
Reduce free space to a one-dimensional
representation.
Lower representation is called a skeleton.
Example is a Voronoi graph. (points equidistant
to two or more obstacles).
Steps:
-) Follow Voronoi graph until close to target
-) Leave graph and move to target.

39. Probabilistic Roadmap
Create random graph by creating a large
number of configurations.
Discard those that do not fall into free space.
Then join any two nodes by an arc if it is easy
to reach one node from the other.
Method is incomplete but scales better to high
dimensional configurations.

41. Robotics
Introduction
Robot Hardware
Robotic Perception
Planning to Move
Dynamics and Control
Robotic Software
Applications

42. Dynamics and Control
Keeping a robot on track is not easy.
Use a controller to keep the robot on track.
Controllers that provide a force in negative
proportion to the observed error are known
as P controllers.

44. Dynamics and Control
Let y(t) be the reference path.
The control generated by the controller
has the form:
a(t) = K ( y(t) – x(t) )
K: gain parameter

46. Dynamics and Control
To achieve stability we use a PD controller
P – proportional
D – derivative
a(t) = K1 ( y(t) – x(t) ) + K2 d ( y(t) - x(t) ) / dt
K1: gain parameter
K2: differential component

48. Reactive Control
In some cases reflex-agents are more appropriate.
When a leg’s forward motion is blocked,
Simply retract it, lift it higher,
And try again.

50. Robotics
Introduction
Robot Hardware
Robotic Perception
Planning to Move
Dynamics and Control
Robotic Software
Applications

51. Robotic Software
Three layer architecture
reactive layer
( low-level control)
executive layer
(which reactive behavior to invoke?)
deliberate layer
(planning)

52. Robotics
Introduction
Robot Hardware
Robotic Perception
Planning to Move
Dynamics and Control
Robotic Software
Applications

53. Applications
Industry and Agriculture
Assembly lines
Harvest, Mine
Excavate earth
Transportation
Autonomous helicopters
Automatic wheelchairs
Transport food in hospitals

54. Applications
Hazardous environments
Cleaning up nuclear waste
Collapse of World Trade Center
Transport bombs
Exploration
Surface of Mars
Under the sea
Military activities
Health Care (surgery)
Personal Services

55. Applications
Health Care
Surgery
Personal Services
Entertainment
Dog-like robots
Human Augmentation