AI Lab Weekly Seminar By: Buluç Çelik

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 Multi- Agent Planning Algorithms

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. Part I A Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields

Outline ► 1. Introduction ► 2. Architecture ► 3. Modeling of the Environment ► 4. Motion Behaviors ► 5. Behaviors for Action Evaluation ► 6. Applications ► 7. Conclusion & Future Works A Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields

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 A Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields

Introduction ► Behavior-based Architectures  The proposed approach combines existing approaches in a behavior based architecture by realizing single competing behaviors as potential fields A Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields

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 stand- alone architecture A Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields

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 A Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields

Modeling of the Environment ► ► The architecture offers various options allowing a detailed description   An object class O : O = (f O, G O, F O ) ► ► f O : potential function (e.g. attractive, repulsive) ► ► G O : geometric primitive used to approximate an object’s shape ► ► F O : the kind of field (e.g. circumfluent around G O, tangential around the position of the instance) A Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields

Motion Behaviors ► ► The general procedure of motion planning  A vector v can be computed by the superposition of the force vectors v i of all n object instances assigned to a behavior ► R is the current position of the robot ► v can be used to determine the robot’s direction of motion, rotation and speed A Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields

Motion Behaviors A Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields ► 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)

Motion Behaviors A Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields ► 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 configuration is a local minimum of the total potential function

Motion Behaviors ► Dealing with local minima  A* algorithm is used, the continuous environment is discretized A Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields  A search tree with a dynamic number and size of branches is build up

Behaviors for Action Evaluation A Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields ► 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

Behaviors for Action Evaluation A Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields ► φ(P) may be determined at an arbitrary position P, being the sum of the potential functions of all object instances assigned to the behavior ► 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

Applications A 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

Conclusion & Future Works A 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 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

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. Part II Real-Time Object Recognition Using Decision Tree Learning

Outline ► 1. Introduction ► 2. The Recognition Process ► 3. Results ► 4. Conclusion Real-Time Object Recognition Using Decision Tree Learning

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 Real-Time Object Recognition Using Decision Tree Learning

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 Real-Time Object Recognition Using Decision Tree Learning

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 Real-Time Object Recognition Using Decision Tree Learning

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 Real-Time Object Recognition Using Decision Tree Learning

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 χ 2 -pruning Real-Time Object Recognition Using Decision Tree Learning

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 Real-Time Object Recognition Using Decision Tree Learning

Results Real-Time Object Recognition Using Decision Tree Learning

Results Real-Time Object Recognition Using Decision Tree Learning

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 Learning

My Thesis Comparison of Multi-Agent Planning Algorithms Part III Comparison of Multi-Agent Planning Algorithms

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 Comparison of Multi-Agent Planning Algorithms

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 Comparison of Multi-Agent Planning Algorithms

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