Integration of
Representation Into GoalDriven Behavior-Based
Robots
By Dr. Maja J. Mataric`
Presented by Andy Klempau
Introduction
Hybrid system separates reactive low level
actions from deliberative decision actions.
Alternative to Hybrid approach is BehaviorBased system.
Behavior-Based system combines a reactive
subsumption foundation with a decision maker.
Toto is a Behavior-Based robot that explores
dynamic office environments, identifies
landmarks, maps the landmarks, and uses the
map for path planning.
Outline
Toto’s Structure
Basic Navigation ( Exploring )
Landmark Detection
Mapping Landmarks
Path Planning
Toto’s Structure
Cylindrical Robot
12 Sonar Sensors
4 Bit Compass (16 states)
Basic Navigation
Subsumption Architecture
Highest priority is Stroll behavior
Lowest priority is Correct behavior
Basic Navigation
Stroll :
if( min( sonars 1 2 3 4 ) <= danger-zone )
if( not stopped )
stop
else
move backward
else
move forward
Basic Navigation
Avoid :
if( ( sonar 3 or 4 ) <= safe-zone )
turn left
else if( ( sonar 1 or 2 ) <= safe-zone )
turn right
Basic Navigation
Align :
if( ( sonar 7 or 8 ) < edging-distance AND
( sonar 5 or 6 ) > edging-distance )
turn right
if( ( sonar 9 or 10 ) < edging-distance AND
( sonar 11 or 0 ) > edging-distance )
turn left
Basic Navigation
Correct :
if( sonar 11 < edging-distance AND
sonar 0 > edging-distance )
turn left
if( sonar 6 < edging-distance AND
sonar 5 > edgingdistance )
turn right
Landmark Detection
4 types of landmarks:




Right wall (RW); consistent right wall and consistent
direction.
Left wall (LW); consistent left wall and consistent
direction.
Corridor (C); consistent left and right walls and
consistent direction.
Irregular (I); inconsistent walls and inconsistent
direction.
How does Toto identify landmarks?
Confidence
Counter!
Landmark Detection
After a time interval, sonar and compass
readings are taken.
Confidence Counter increments when
sonar and compass readings are the same
as last time interval.
Predetermined threshold identifies how
many time intervals are needed to justify a
landmark
Landmark Detection
Mapping Landmarks
After discovered, landmarks are stored in Toto’s
internal map.
Landmark nodes store information discovered
through sensors and compass (see next slide).
Nodes communicate with neighbors.
Mapping Landmarks
Landmark node has a set < T, C, L, P > where
T is { LW, RW, C, I }; qualitative landmark type.
C is [ 0 … 15 ]; averaged compass bearing.
L is [ 1 … 127 ]; rough estimate of landmark’s length.
P = ( x, y ) -128 <= x, y <= 127; coarse position estimate.
Length is obtained through timer (could be confidence
counter).
Position is obtained through length and
compass.
Mapping Landmarks
Example:
Path Planning
Use the map to go to a goal.
This is done by activating one of Toto’s
previously visited landmarks as a goal.
Path Planning
Goal sends signal to neighbor nodes.
Eventually, all nodes know where goal is.
Greedy algorithm ensures Toto will take
shortest path to goal.
Toto can go to goal starting from any landmark.
Toto can adapt to a changing environment.
Review
Explores
Finds landmarks
Stores landmarks in
map
Goes to goal
Conclusion
Toto explores, maps, plans, and finds
goals without Deliberative or Hybrid
process.
Toto “extends the repertoire of integrated
reactive systems to tasks requiring spatial
modeling and user interaction.”
Toto can adapt to a dynamic environment.
Discussion
Is linear-time path planning reactive?
Can a Behavior-Based system do anything
a hybrid system can do?
How is the open-space behavior
triggered?