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25.03.200525.03.2005 11
AI Lab AI Lab Weekly SeminarWeekly Seminar
By: Buluç ÇelikBy: Buluç Çelik
25.03.2005 2
General OutlineGeneral Outline
►Part I: Part I: A Behavior Architecture for A Behavior Architecture for Autonomous Mobile Robots Based on Potential Autonomous Mobile Robots Based on Potential FieldsFields
►Part II: Part II: Real-Time Object Recognition Using Real-Time Object Recognition Using Decision Tree LearningDecision Tree Learning
►Part III: Part III: My Thesis - Comparison of Multi-My Thesis - Comparison of Multi-Agent Planning AlgorithmsAgent Planning Algorithms
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A Behavior Architecture for AutonomousA Behavior Architecture for AutonomousMobile Robots Based on Potential FieldsMobile Robots Based on Potential Fields
Laue, T., Röfer, T. (2005)Laue, T., Röfer, T. (2005)
In: 8th International Workshop on RoboCup In: 8th International Workshop on RoboCup 2004 (Robot World Cup Soccer Games and 2004 (Robot World Cup Soccer Games and
Conferences), Conferences),
Lecture Notes in Artificial Intelligence. Lecture Notes in Artificial Intelligence. Springer, im Erscheinen.Springer, im Erscheinen.
Part IPart I
A Behavior Architecture for Autonomous Mobile Robots Based on Potential FieldsA Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields
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OutlineOutline
► 1. Introduction1. Introduction
► 2. Architecture2. Architecture
► 3. Modeling of the Environment3. Modeling of the Environment
► 4. Motion Behaviors4. Motion Behaviors
► 5. Behaviors for Action Evaluation5. Behaviors for Action Evaluation
► 6. 6. ApplicationsApplications
► 7. 7. Conclusion & Future WorksConclusion & Future Works
A Behavior Architecture for Autonomous Mobile Robots Based on Potential FieldsA Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields
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1. Introduction1. Introduction
►Artificial Potential FieldsArtificial Potential Fields Popular for being capable of acting in Popular for being capable of acting in
continuous domains in real timecontinuous domains in real time Can follow a collision-free path via the Can follow a collision-free path via the
computation of a motion vector from the computation of a motion vector from the superposed force fieldssuperposed force fields►Repulsive force fields to obstaclesRepulsive force fields to obstacles►Attractive force fields to desired destinationAttractive force fields to desired destination
A Behavior Architecture for Autonomous Mobile Robots Based on Potential FieldsA Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields
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1. Introduction1. Introduction
►Behavior-based ArchitecturesBehavior-based Architectures
The proposed approach combines existing The proposed approach combines existing approaches in a behavior based approaches in a behavior based architecture by realizing single competing architecture by realizing single competing behaviors as potential fieldsbehaviors as potential fields
A Behavior Architecture for Autonomous Mobile Robots Based on Potential FieldsA Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields
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2. Architecture2. Architecture
►Potential fields are based on Potential fields are based on superposion of force fieldssuperposion of force fields Fails for tasks with more than one Fails for tasks with more than one
possible goal position (e.g. goalkeeper)possible goal position (e.g. goalkeeper) Could be solved by selecting the most Could be solved by selecting the most
appropriate goalappropriate goal►But this proceeding will affect the claim of But this proceeding will affect the claim of
stand-alone architecturestand-alone architecture
A Behavior Architecture for Autonomous Mobile Robots Based on Potential FieldsA Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields
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2. Architecture2. Architecture
►Different tasks have to be splitted into Different tasks have to be splitted into different competing behaviorsdifferent competing behaviors
► Among the blocking and keeping of Among the blocking and keeping of behaviors under certain circumstances, behaviors under certain circumstances, behaviors can be combined with others to behaviors can be combined with others to realize small hierarchiesrealize small hierarchies For instance, this allows the usage of a number For instance, this allows the usage of a number
of evaluation behaviors differentiating situations of evaluation behaviors differentiating situations (e. g. defense or midfield play in robot soccer) (e. g. defense or midfield play in robot soccer) respectively combined with appropriate motion respectively combined with appropriate motion behaviorsbehaviors
A Behavior Architecture for Autonomous Mobile Robots Based on Potential FieldsA Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields
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3. Modeling of the 3. Modeling of the EnvironmentEnvironment
►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)
A Behavior Architecture for Autonomous Mobile Robots Based on Potential FieldsA Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields
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4. Motion Behaviors4. Motion Behaviors
►The general procedure of motion planning A vector A vector vv can be computed by the can be computed by the
superposition of the force vectors superposition of the force vectors vvii of all of all nn object instances assigned to a behaviorobject instances assigned to a behavior
►RR is the current position of the robot is the current position of the robot►v v can be used to determine the robot’s direction of can be used to determine the robot’s direction of
motion, rotation and speedmotion, rotation and speed
A Behavior Architecture for Autonomous Mobile Robots Based on Potential FieldsA Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields
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4. Motion Behaviors4. Motion Behaviors
A Behavior Architecture for Autonomous Mobile Robots Based on Potential FieldsA Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields
► Relative motionsRelative 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 Behaviors4. Motion Behaviors
A Behavior Architecture for Autonomous Mobile Robots Based on Potential FieldsA Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields
►Dealing with local minimaDealing with local minima Local minima are an inherent problem of
potential fields, an optimal standard solution does not exist►The attractive potential The attractive potential
guides the robot’s path guides the robot’s path into a C-obstacle concavityinto a C-obstacle concavity
►At some point, the At some point, the repulsive force cancels repulsive force cancels exactly the attractive forceexactly the attractive force
►This stable zero-force This stable zero-force
configuration is a local minimum of the total potential configuration is a local minimum of the total potential functionfunction
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4. Motion Behaviors4. Motion Behaviors
►Dealing with local minimaDealing with local minima A* algorithm is used, the continuous A* algorithm is used, the continuous
environment is discretizedenvironment is discretized
A Behavior Architecture for Autonomous Mobile Robots Based on Potential FieldsA Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields
A search tree A search tree with a dynamic with a dynamic number and number and size of size of branches is branches is build upbuild up
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5. Behaviors for Action 5. Behaviors for Action EvaluationEvaluation
A Behavior Architecture for Autonomous Mobile Robots Based on Potential FieldsA Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields
► An action behavior can be combined with a An action behavior can be combined with a motion behavior to determine the motion behavior to determine the appropriateness of its executionappropriateness of its execution
► The environment is rasterized into cells of The environment is rasterized into cells of fixed sizefixed size
► The anticipated world state after an action is The anticipated world state after an action is computedcomputed
► The value of only the relevant positions are The value of only the relevant positions are evaluated to determine the most appropriate evaluated to determine the most appropriate positionposition
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5. Behaviors for Action 5. Behaviors for Action EvaluationEvaluation
A Behavior Architecture for Autonomous Mobile Robots Based on Potential FieldsA Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields
► φ(P)φ(P) may be determined at an arbitrary position may be determined at an arbitrary position P, being the sum of the potential functions of all P, being the sum of the potential functions of all object instances assigned to the behaviorobject instances assigned to the behavior
► evaluate a certain action which changes the evaluate a certain action which changes the environment (e. g. kicking a ball) this action has environment (e. g. kicking a ball) this action has to be mapped to a geometric transformation (e. to be mapped to a geometric transformation (e. g. rotation, translation) in order to describe the g. rotation, translation) in order to describe the motion of the manipulated objectmotion of the manipulated object
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6. Applications6. Applications
A Behavior Architecture for Autonomous Mobile Robots Based on Potential FieldsA Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields
► 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 Works7. Conclusion & Future Works
A Behavior Architecture for Autonomous Mobile Robots Based on Potential FieldsA Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields
► 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 worksFuture works Porting the architecture to other platforms to test and extend Porting the architecture to other platforms to test and extend
the capabilities of this approachthe capabilities of this approach There exist several features already implemented but not There exist several features already implemented but not
adequately tested (e. g. the integration of object instances adequately tested (e. g. the integration of object instances based on a probabilistic world model)based on a probabilistic world model)
In addition, the behavior selection process is currently In addition, the behavior selection process is currently extended to deal with a hierarchy of sets of competing extended to deal with a hierarchy of sets of competing behaviors, allowing the specification of even more complex behaviors, allowing the specification of even more complex overall behaviorsoverall behaviors
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Real-Time Object Recognition Using Decision Real-Time Object Recognition Using Decision Tree LearningTree Learning
Wilking, D., Röfer, T. (2005)Wilking, D., Röfer, T. (2005)
In: 8th International Workshop on RoboCup In: 8th International Workshop on RoboCup 20042004
(Robot World Cup Soccer Games and (Robot World Cup Soccer Games and Conferences),Conferences),
Lecture Notes in Artificial Intelligence. Lecture Notes in Artificial Intelligence. Springer, im Erscheinen.Springer, im Erscheinen.
Part IIPart II
Real-Time Object Recognition Using Decision Tree LearningReal-Time Object Recognition Using Decision Tree Learning
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OutlineOutline
►1. Introduction1. Introduction
►2. The Recognition Process2. The Recognition Process
►3. Results3. Results
►4. Conclusion4. Conclusion
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1. Introduction1. 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 Process2. The Recognition Process
► The recognition begins with iterating through thesurfaces that have been discoveredby the preprocessing stage
► For every surface, a numberof segments approximatingits shape and a symbol isgenerated (e. g. head, side,front, back, leg, or nothing)
► The symbols are inserted intoa special 180 symbol memory
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2. The Recognition Process2. 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 Process2. 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 Process2. 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 Process2. 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. Results3. Results
Real-Time Object Recognition Using Decision Tree LearningReal-Time Object Recognition Using Decision Tree Learning
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3. Results3. Results
Real-Time Object Recognition Using Decision Tree LearningReal-Time Object Recognition Using Decision Tree Learning
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4. Conclusion4. 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
Real-Time Object Recognition Using Decision Tree LearningReal-Time Object Recognition Using Decision Tree Learning
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My ThesisMy ThesisComparison of Multi-Agent Planning Comparison of Multi-Agent Planning
AlgorithmsAlgorithms
Part IIIPart III
Comparison of Multi-Agent Planning AlgorithmsComparison of Multi-Agent Planning Algorithms
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Comparison of Multi-AgentComparison of Multi-AgentPlanning AlgorithmsPlanning 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-AgentComparison of Multi-AgentPlanning AlgorithmsPlanning 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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DiscussionDiscussion
Thank You...Thank You...