The agilo autonomous robot soccer team computational principles experiences and perspectives
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The AGILO Autonomous Robot Soccer Team: Computational Principles, Experiences, and Perspectives. Michael Beetz, Sebastian Buck, Robert Hanek, Thorsten Schmitt, and Bernd Radig Munich University of Technology

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The AGILO Autonomous Robot Soccer Team: Computational Principles, Experiences, and Perspectives

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The agilo autonomous robot soccer team computational principles experiences and perspectives

The AGILO Autonomous Robot Soccer Team: Computational Principles, Experiences, and Perspectives

Michael Beetz, Sebastian Buck, Robert Hanek, Thorsten Schmitt, and Bernd Radig

Munich University of Technology

In Procs. of the First International Conference on Autonomous Agents and Multi-agent Systems

Presented By: Jonatan Gomez


Outline

Outline

  • Introduction

  • Environment

  • Sensors and Perceptions

  • Drives and Goals

  • Action-Selection Mechanisms (Control)

  • Conclusions

  • References


Introduction

Introduction

  • This paper describes:

    • The computational model underlying the AGILO autonomous robot soccer team

    • The AGILO implementation

    • Some experience with it.


Introduction1

Introduction

  • In robot soccer (mid-size league):

    • Two teams of autonomous robots play soccer against each other.

    • Each team has four members - one goal keeper and three field players.

    • The soccer field is 4 * 9 meters big surrounded by walls.


Introduction2

Introduction

  • Skillful play requires the robots:

    • Recognize objects, such as other robots, field lines, and goals.

    • Recognize entire game situations

    • Collaborate by coordinating and synchronizing their actions to achieve their objectives.


Introduction3

Introduction

  • The AGILO robot controllers employ:

    • Game state estimation

    • Situated action selection

    • Playbook execution


Environment

Environment

  • A soccer field with the following characteristics:

    • 4 * 9 meters big

    • Surrounded by walls

    • Field lines

    • 8 autonomous robots

    • 2 Goals

    • 1 ball


Environment1

Environment

  • Properties

    • Dynamic

    • Semi-Accessible

    • Non-deterministic

    • Non-episodic

    • Continuous


Sensors and perceptions

Sensors and Perceptions

  • A wireless ethernet for communication with others team members (1).

  • Sonar sensors for collision avoidance (4).

  • A fixed color CCD camera with an opening angle of 90o (3).


Sensors and perceptions1

Sensors and Perceptions

  • Vision-based, Cooperative Game State Estimation

    • Video Perception

    • Self Localization

    • Opponent Tracking

    • Cooperative State Estimation


Sensors and perceptions2

Sensors and Perceptions


Sensors and perceptions3

Sensors and Perceptions

Video Perception


Sensors and perceptions4

Sensors and Perceptions

Self Localization


Drives and goals

Drives and Goals

  • Drives:

    • Ultimate: Win the Game.

    • Maximal: Score in the other team goal.

    • Maximal: Do not allow to the other team score in its goal.

    • Research: Show some kind of cooperative and collective behavior.


Drives and goals1

Drives and Goals

  • Goals (Tasks): Intention of the AGILO robot team to perform a certain actions.

    • Shoot the ball into the goal

    • Dribble the ball towards the goal

    • Look for the ball

    • Block the way to the goal

    • Get the ball

    • …

  • Each goal has an associated priority


Action selection mechanisms

Action-Selection Mechanisms

  • Action-Selection (and execution) is constrained by:

    • Goals being achievable only if certain conditions hold (eg, the robot has the ball)

    • A robot is able to execute only one action at the same time


Action selection mechanisms1

Action-Selection Mechanisms

  • Situated Action-Selection:

    • A goal assignment is a list of goals that an individual robot can perform (according to the goals priority and cost).

    • An order over the goal assignments is imposed in order to determine the goals to be performed by a robot.


Action selection mechanisms2

Action-Selection Mechanisms

  • Situated Action-Selection:

    • A is better than B if there is a goal in B that has lower priority that all the ones in A or they achieve the same goals but there exists a goal t in A such that all goals with higher priority are performed at least at fast as in B and t is achieved faster by A than by B


Action selection mechanisms3

Action-Selection Mechanisms

  • Situated Action-Selection: The goal (task) cost estimator perform three steps:

    • Selection of the multi-robot navigation method that matches the game state best

    • Computing a path in the context of the navigation paths of the team mates

    • The proposed path is decomposed into a sequence of simpler navigation tasks for which the time cost can be accurately predicted using a neural network.


Action selection mechanisms4

Action-Selection Mechanisms


Action selection mechanisms5

Action-Selection Mechanisms

  • Situated Action-Selection:

    • Choosing actions that have the highest expected utility in the respective situation

    • Does not take into account a strategic assessment of alternative actions.

    • In general is a limited temporal horizon.


Action selection mechanisms6

Action-Selection Mechanisms

  • Plan Based Control:

    • Improve the robot soccer team by adding the capability of learning and execute soccer plays.

    • Soccer plays are properly synchronized, cooperative macro actions than can be executed in certain game contexts and have, in these contexts, a high success rate.


Action selection mechanisms7

Action-Selection Mechanisms

  • Plan Based Control:

    • A robot soccer playbook, a library of plan schema data that specify how to perform individual team plays, is added to each robot.

    • The plans are triggered by opportunities, for example, the opponent team leaving a side open.

    • The plays specify highly reactive, conditional, and properly synchronized behavior for the individuals players of the team.


Action selection mechanisms8

Action-Selection Mechanisms

Plan Based Control


Conclusions

Conclusions

  • This paper described and discussed the control software of the AGILO autonomous robot soccer team.

  • The AGILO teams employs sophisticated state estimation and control techniques, including experience-based learning and plan-based control mechanisms.


References

References

  • M. Beetz. Structured Reactive Controllers. Journal of Autonomous Agents and Multi-Agent Systems, 4:25-55, March/June 2001.

  • Sebastian Buck, Michael Beetz, and Thorsten Schmitt. Planning and Executing Joint Navigation Tasks in Autonomous Robot Soccer citeseer.nj.nec.com/445873.html

  • Sebastian Buck and Michael Beetz and Thorsten Schmitt. M-ROSE: A Multi Robot Simulation Environment for Learning Cooperative Behavior citeseer.nj.nec.com/buck02mrose.html

  • http://www.cs.kuleuven.ac.be/~nico/demo/pages/rcvideo.html


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