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AI Lab
Weekly Seminar
By: Buluç Çelik
25.03.2005
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General Outline
► Part
I: A Behavior Architecture for Autonomous
Mobile Robots Based on Potential Fields
► Part
II: Real-Time Object Recognition Using
Decision Tree Learning
► Part
III: My Thesis - Comparison of MultiAgent Planning Algorithms
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Part I
A Behavior Architecture for Autonomous
Mobile Robots Based on Potential Fields
Laue, T., Röfer, T. (2005)
In: 8th International Workshop on RoboCup 2004
(Robot World Cup Soccer Games and Conferences),
Lecture Notes in Artificial Intelligence. Springer, im
Erscheinen.
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A Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields
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Outline
► 1.
Introduction
► 2.
Architecture
► 3.
Modeling of the Environment
► 4.
Motion Behaviors
► 5.
Behaviors for Action Evaluation
► 6.
Applications
► 7.
Conclusion & Future Works
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A Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields
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1. Introduction
► Artificial
Potential Fields
 Popular for being capable of acting in
continuous domains in real time
 Can follow a collision-free path via the
computation of a motion vector from the
superposed force fields
►Repulsive
force fields to obstacles
►Attractive force fields to desired destination
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1. Introduction
► Behavior-based
Architectures
 The proposed approach combines existing
approaches in a behavior based architecture by
realizing single competing behaviors as potential
fields
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2. Architecture
► Potential
fields are based on superposion of
force fields
 Fails for tasks with more than one possible goal
position (e.g. goalkeeper)
 Could be solved by selecting the most
appropriate goal
►But
this proceeding will affect the claim of standalone architecture
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2. Architecture
► Different
tasks have to be splitted into different
competing behaviors
► Among the blocking and keeping of behaviors
under certain circumstances, behaviors can be
combined with others to realize small hierarchies
 For instance, this allows the usage of a number of
evaluation behaviors differentiating situations (e. g.
defense or midfield play in robot soccer) respectively
combined with appropriate motion behaviors
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3. Modeling of the Environment
► The
architecture offers various options
allowing a detailed description
 An object class O: O = (fO, GO, FO)
►fO :
potential function (e.g. attractive, repulsive)
►GO : geometric primitive used to approximate an
object’s shape
►FO : the kind of field (e.g. circumfluent around GO ,
tangential around the position of the instance)
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4. Motion Behaviors
► The
general procedure of motion planning
 A vector v can be computed by the
superposition of the force vectors vi of all n
object instances assigned to a behavior
n
v   vi R 
i 1
is the current position of the robot
►v can be used to determine the robot’s direction of
motion, rotation and speed
►R
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4. Motion Behaviors
► Relative
motions
 Assigning force fields to single objects of the
environment allows the avoidance of obstacles and the
approach to desired goal positions
 Moving to more complex spatial configurations (e. g.
positioning between the ball and the penalty area or
lining up with several robots) is not possible directly
 Relative motions are realized via special objects which
may be assigned to behaviors
► Such
an object consists of a set of references to object
instances and a spatial relation (e. g. between, relative-angle)
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4. Motion Behaviors
► Dealing
with local minima
 Local minima are an inherent problem of potential
fields, an optimal standard solution does not exist
► The
attractive potential
guides the robot’s path into a
C-obstacle concavity
► At some point, the repulsive
force cancels exactly the
attractive force
►This stable zero-force
configuration is a local minimum of the total potential function
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4. Motion Behaviors
► Dealing
with local minima
 A* algorithm is used, the continuous environment is
discretized
 A search tree
with a dynamic
number and
size of
branches is
build up
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5. Behaviors for Action Evaluation
► An
action behavior can be combined with a motion
behavior to determine the appropriateness of its
execution
► The environment is rasterized into cells of fixed size
► The anticipated world state after an action is
computed
► The value of only the relevant positions are
evaluated to determine the most appropriate
position
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5. Behaviors for Action Evaluation
may be determined at an arbitrary position P,
being the sum of the potential functions of all object
instances assigned to the behavior
► φ(P)
n
 P     i P 
i 1
► evaluate
a certain action which changes the
environment (e. g. kicking a ball) this action has to
be mapped to a geometric transformation (e. g.
rotation, translation) in order to describe the motion
of the manipulated object
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6. Applications
►
The architecture has been applied to two different
platforms, both being RoboCup teams of the
Universit¨at Bremen
 Robots of the Bremen Byters, which are a part of the
GermanTeam (Sony Four-legged Robot League)
 The control program of B-Smart, which competes in the
RoboCup F-180 (Small Size) league
For playing soccer, about 10–15 behaviors have been
needed (e. g. go to Ball, go to defense position or kick
ball forward)
► Sequences of actions have been used, allowing a quite
forward-looking play
►
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7. Conclusion & Future Works
►
A behavior-based architecture
 For autonomous mobile robots
 Integrating several different approaches for motion planning and
action evaluation into a single general framework
 Dividing different tasks into competing behaviors
►
Future works
 Porting the architecture to other platforms to test and extend the
capabilities of this approach
 There exist several features already implemented but not adequately
tested (e. g. the integration of object instances based on a
probabilistic world model)
 In addition, the behavior selection process is currently extended to
deal with a hierarchy of sets of competing behaviors, allowing the
specification of even more complex overall behaviors
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Part II
Real-Time Object Recognition Using Decision
Tree Learning
Wilking, D., Röfer, T. (2005)
In: 8th International Workshop on RoboCup 2004
(Robot World Cup Soccer Games and Conferences),
Lecture Notes in Artificial Intelligence. Springer, im
Erscheinen.
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Real-Time Object Recognition Using Decision Tree Learning
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Outline
► 1.
Introduction
► 2.
The Recognition Process
► 3.
Results
► 4.
Conclusion
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Real-Time Object Recognition Using Decision Tree Learning
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1. Introduction
► The
goal of the process presented in this paper is
the computation of the pose of a visible robot
(i. e. the distance, angle, and orientation)
► Apart from the unique color which can be used
easily to find a robot in an image, the geometric
shapes of the different parts provide much more
information about the position of the robot
► The shapes themselves can be approximated using
simple line segments and the angles between
them
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2. The Recognition Process
The recognition begins with iterating through the
surfaces that have been discovered
by the preprocessing stage
► For every surface, a number
of segments approximating
its shape and a symbol is
generated (e. g. head, side,
front, back, leg, or nothing)
► The symbols are inserted into
a special 180 symbol memory
►
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2. The Recognition Process
► Segmentation
and surface detection
 Relevant pixels are determined by color
segmentation using color tables
 Surfaces (along with their position,
bounding box, and area) are computed
 The contour of the surface is computed
 The iterative-end point algorithm is used
to compute the segments
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2. The Recognition Process
► Attribute
generation
 Simple attributes (e. g., color class, area, perimeter,
and aspect ratio)
 Regarding the representation of the surface (e. g.
line segments, the number of corners, the
convexity and the number of different classes of
angles between two line segments)
 The surface is compared to a circle and a rectangle
with the same area
 Sequences of adjacent angles
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2. The Recognition Process
► Classification
 The decision tree learning algorithm is chosen
as classification algorithm
 The tree is built by calculating the attribute with
the highest entropy
 over-fitting is solved using χ2-pruning
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2. The Recognition Process
► Analysis
 The surface area of a group is used
to determine the distance to the
robot
 The direction to the robot is
computed by the group’s position in
the 180 memory
 The relative position of the head
within the group and the existence
of front or back symbols indicate the
rough direction of the robot
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3. Results
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3. Results
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4. Conclusion
►A
robot recognition process based on decision tree
classification
► Due to the complexity and length of the process,
some parts could be streamlined
► The heuristics used during the analysis step can
be improved using a skeleton template based,
probabilistic matching procedure
 deal both with the problem of occlusion and missing
symbols
► improvements
concerning the speed of the
attribute generation can be achieved
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Part III
My Thesis
Comparison of Multi-Agent Planning
Algorithms
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Comparison of Multi-Agent Planning Algorithms
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Comparison of Multi-Agent
Planning Algorithms
► Multi-agent
planning algorithms are to be
designed and implemented for Sony Four-legged
Robot League
► A behavior architecture for autonomous mobile
robots based on potential fields will be designed
and implemented
 One similar to the one explained at Part I
► Training
a neuro-fuzzy system using the designed
behavior architecture
 A neuro-fuzzy system will be trained using the decisions
made by the implementation of behavior architecture
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Comparison of Multi-Agent Planning Algorithms
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Comparison of Multi-Agent
Planning Algorithms
► Training
the neuro-fuzzy system with
playing against the behavior architecture
 The neuro-fuzzy system will be trained more by
playing against the implementation of behavior
architecture
►A
decision tree will be produced from the
neuro-fuzzy network
► The architectures will be evaluated and
compared
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Thank You...
Discussion
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