SLIDE 1 Introduction to Introduction to Mobile Robotics
R b t t l di Robot control paradigm s
Wolfram Burgard Cyrill Stachniss Gi i G i tti Giorgio Grisetti Maren Bennewitz Christian Plagemann
SA-1 SA-1
Christian Plagemann
SLIDE 2 Classical / Hierarchical Paradigm Classical / Hierarchical Paradigm
Sense Plan Act
- 70’s
- Focus on automated reasoning and knowledge
t ti representation
- STRIPS (Stanford Research Institute Problem
Solver): Perfect world model closed world Solver): Perfect world model, closed world assumption
- Find boxes and move them to designated position
SLIDE 3
Shakey ‘6 9 Shakey 6 9
Stanford Research Stanford Research Institute
SLIDE 4
Stanford CART ‘7 3 Stanford CART 7 3
Stanford AI Laboratory / CMU (Moravec)
SLIDE 5
Classical Paradigm St f d C t Stanford Cart
1.
Take nine images of the environment, identify interesting points in one image and use other interesting points in one image, and use other images to obtain depth estimates.
2
Integrate information into global world model
2.
Integrate information into global world model.
3.
Correlate images with previous image set to estimate robot motion estimate robot motion.
4.
On basis of desired motion, estimated motion, and current estimate of environment determine and current estimate of environment, determine direction in which to move.
5
Execute the motion
5.
Execute the motion.
SLIDE 6 Reactive / Behavior-based Paradigm Reactive / Behavior based Paradigm
Sense Act
- No models: The world is its own, best
model b l l
- Easy successes, but also limitations
- Investigate biological systems
SLIDE 7 Classical Paradigm as Horizontal/ Functional Decom position Horizontal/ Functional Decom position n rol eption del an cute Contr
Sense Plan Act
Perce Mo Pla Exec
P Mo
Action Sensing
Environm ent
SLIDE 8
Reactive Paradigm as Vertical Decom position Vertical Decom position
Build map Explore Wander p Avoid obstacles Wander Avoid obstacles Action Sensing Action Sensing
Environm ent
SLIDE 9 Characteristics of Reactive Pa adigm Paradigm
Sit t d t b t i i t l t f th
- Situated agent, robot is integral part of the
world.
- No memory, controlled by what is
happening in the world. pp g
- Tight coupling between perception and
action via behaviors action via behaviors.
- Only local, behavior-specific sensing is
permitted (ego-centric representation).
SLIDE 10 Behaviors Behaviors
are a direct mapping of sensory inputs to pp g y p a pattern of motor actions that are then used to achieve a task.
serve as the basic building block for robotics actions and the overall behavior robotics actions, and the overall behavior
- f the robot is emergent.
- …
support good software design principles due to modularity.
SLIDE 11 Subsum ption Architecture Subsum ption Architecture
- Introduced by Rodney Brooks ’86.
y y
- Behaviors are networks of sensing and
acting modules (augmented finite state acting modules (augmented finite state machines AFSM).
- Modules are grouped into layers of
competence.
- Layers can subsume lower layers.
N i t l t t !
SLIDE 12 Level 0 : Avoid Level 0 : Avoid
Polar plot of sonars Feel force Run away
Turn
force heading
Sonar
polar plot force heading heading encoders
Collide Forward
p halt
SLIDE 13 Level 1 : W ander Level 1 : W ander
Wander Avoid heading Wander Avoid force modified heading Feel force Run away
Turn
force heading
s
Sonar
polar plot force heading heading encoders
Collide Forward
p halt
SLIDE 14 Level 2 : Follow Corridor
heading to middle
Look
Stay in middle corridor
Integrate
distance, direction traveled
Wander Avoid
to middle
s
corridor
Wander Avoid force modified heading Feel force Run away
Turn
force
s
heading
Sonar
polar plot force heading heading encoders
Collide Forward
p halt
SLIDE 15 Potential Field Methodologies Potential Field Methodologies
- Treat robot as particle acting under the
p g influence of a potential field
- Robot travels along the derivative of the
- Robot travels along the derivative of the
potential
- Field depends on obstacles desired travel
- Field depends on obstacles, desired travel
directions and targets R lti fi ld ( t ) i i b th
- Resulting field (vector) is given by the
summation of primitive fields
- Strength of field may change with distance
to obstacle/ target
SLIDE 16
Prim itive Potential Fields Prim itive Potential Fields
Uniform Perpendicular Attractive Repulsive Tangential p g
SLIDE 17 Corridor follow ing w ith Potential Fields Potential Fields
- Level 0 (collision avoidance)
Level 0 (collision avoidance)
is done by the repulsive fields of detected
- bstacles.
- bstacles.
- Level 1 (wander)
adds a uniform field.
- Level 2 (corridor following)
- Level 2 (corridor following)
replaces the wander field by three fields (two perpendicular one uniform) (two perpendicular, one uniform).
SLIDE 18 Characteristics of Potential Fields Characteristics of Potential Fields
- Suffer from local minima
- Suffer from local minima
G l
Goal
- Backtracking
- Random motion to escape local minimum
- Procedural planner s.a. wall following
Procedural planner s.a. wall following
- Increase potential of visited regions
- Avoid local minima by harmonic functions
y
SLIDE 19 Characteristics of Potential Fields Characteristics of Potential Fields
- No preference among layers
- No preference among layers
- Easy to visualize
- Easy to visualize
- Easy to combine different fields
asy to co b e d e e t e ds
- High update rates necessary
g p y
- Parameter tuning important
SLIDE 20 Reactive Paradigm Reactive Paradigm
Representations?
- Good software engineering principles?
- Easy to program?
- Robustness?
S l b l ?
SLIDE 21 Hybrid Deliberative/ reactive Paradigm Paradigm
Plan S A t Sense Act
- Combines advantages of previous paradigms
- World model used for planning
- World model used for planning
- Closed loop, reactive control
SLIDE 22 Discussion Discussion
- Imagine you want your robot to
Imagine you want your robot to perform navigation tasks, which approach would you choose? approach would you choose?
- What are the benefits of the behavior
What are the benefits of the behavior based paradigm?
- Which approaches will win in the long
run? run?
