25.03.20051 AI Lab Weekly Seminar By: Buluç Çelik

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AI Lab AI Lab Weekly SeminarWeekly Seminar

By: Buluç ÇelikBy: Buluç Çelik

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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


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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


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►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


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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


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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


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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)


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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


n

ii Rvv

1

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► 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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►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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►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 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


► 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


► 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

n

ii PP

1

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6. Applications6. 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 Works7. 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 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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►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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►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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►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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►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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►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


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3. Results3. Results


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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


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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

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25.03.20051 AI Lab Weekly Seminar By: Buluç Çelik