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3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
3AMIGAS - Paper4: Rosario De Chiara
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3AMIGAS - Paper4: Rosario De Chiara

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Presentation at the 3AMIGAS workshop

Presentation at the 3AMIGAS workshop

Published in: Technology, Education
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  • 1. Alberto Boccardo, Rosario De Chiara, Vittorio Scarano ISISLab Dipartimento di Informatica ed Applicazioni “R.M. Capocelli” Università degli Studi di Salerno
  • 2. Agenda • Introduction • Boid Model • Coordinated movement • Conclusion
  • 3. Introduction • The simulation of groups of characters moving in a virtual world is a topic that has been investigated since the 1980s. – Early works take inspiration from particles system [3, 4]
  • 4. Introduction Gravity Initial Aging velocity Emitter Death
  • 5. Introduction • The particle system model can be expanded with the purpose of simulating a group of more complex entities, dubbed autonomous agents: – Movements are related to social interactions among group members: • Example: the simulation a flock (e.g. birds, fishes, aliens, people…) in the most natural possible way.
  • 6. Introduction Gravity Social interactions Initial Aging velocity Emitter Death Fly Aging around Birth Death
  • 7. Introduction We implemented a system capable of animating autonomous agents with the purpose of reconstructing interactive scenes from a battlefield showing a number platoons
  • 8. Introduction Platoons are able to march following a path;
  • 9. Introduction Platoons are capable of engaging a fight with enemy platoons:
  • 10. Introduction Platoons present different soldier topologies deploying different kinds of weapons;
  • 11. The Idea • We expanded the boid model in order to reach an higher degree of complexity of the behaviors: – The initial idea of simulating a flock of boids will be expanded to simulate platoons of soldiers obeying to commands imparted by a leader.
  • 12. Boid model • The boid model simulates the coordinated animal motion such as bird flocks and fish schools – The basic flocking model consists of three simple steering behaviors which describe how an individual boid maneuvers based on the positions and velocities its nearby flockmates: Separation Alignment Cohesion
  • 13. Boid model • The boid model can be expanded by assembling basic behaviors to obtain more complex behaviors: – Seek and Flee – Pursuit and Evade – Obstacle Avoidance Seek and Flee Pursuit and Evade Obstacle avoidance
  • 14. Boid model Enemy pursuit, shooting, Combat evasion commands L/R flank, L/R Face, March Directional forward, at ease commands Flee, Pursuit, Offset Flee, Pursuit, Offset Basic Pursuit, Seek, Evade Pursuit, Seek, Evade behaviors Alignment, Separation, Alignment, Separation, Alignment, Separation, Boid model Cohesion Cohesion Cohesion behaviors Reynolds 1987 [3] Reynolds 1988 This paper Reynolds 1999 [4]
  • 15. Coordinated movement Our system handles 4 elements that made up a simulation: – The map: an heightmap – Obstacles: solid 3d objects – The Leader: one unit per platoon in charge of the decisions on directions and battle – Units: soldier divided in one or more platoons
  • 16. Coordinated movement The leader • The leader knows the path the platoon has to follow and will impart suitable commands to units – The path is just a sequence of checkpoints
  • 17. Coordinated movement The leader To choose the correct directional command the leader compares platoon current direction and the position of next checkpoint on path Forward Next march checkpoint Right direction flank Platoon front Right face Rear march
  • 18. Coordinated movement Assembling Behaviors Each directional command is translated to a series SCRIPT SCRIPT of basic behaviors LEADER Fall in Forward march Right flank Left flank UNITS AVOIDANCE AVOIDANCE ROTATION OBSTACLE PURSUIT OFFSET OFFSET SEEK SEEK FLEE BASIC BEHAVIORS LIBRARY
  • 19. Coordinated movement Combat Mode • The system simulates scenarios in which two or more adversary platoons are present – Each platoon belongs to an army; this is used to discriminate among friend and foe platoons – Soldiers deploy one out of three available models of weapons: melee weapon model, mortar weapon model and rifle weapon model;
  • 20. Coordinated movement Combat Mode • Once an enemy platoon is visible by the leader tells the platoon to switch to combat mode – Each soldiers will decide on its own when to shoot and who to aim to, depending on its position and the model of weapon it is deploying
  • 21. Conclusion • Massive Battle is a framework – Written in C++ – Scenarios are described by a script file • The script file contains a full description of the initial setting of the parameters for each platoon • Once a file is parsed the simulation starts – Every behavior is controlled by configuration/script files
  • 22. Conclusion • Massive Battle is a framework – Does not depends on how the scene is rendered • Current demo uses Ogre3D – It uses a library of basic behaviors • Took from [11] • It is compatible with OpenSteer
  • 23. Conclusion Performances • On a off-the-shelf PC: – AMD Athlon 64 x2 4200+ – 2GB of RAM – ATI X1900 with 512MB The system animates a scene containing 3000 units on an interactive framerate of 25 fps
  • 24. Conclusion Future Work • We are currently working at a GPU accellerated version of the system – You may want to check : “A GPU-based Method for Massive Simulation of Distributed Behavioral Models with CUDA” Ugo Erra, Bernardino Frola, Vittorio Scarano CASA09 short paper !! ☺
  • 25. Alberto Boccardo, Rosario De Chiara, Vittorio Scarano http://www.isislab.it
  • 26. References [1] [1] Couzin ID, Krause J, Franks NR, Levin SA. Nature. 2005 Feb 3;433(7025):513-6. Effective leadership and decision-making in animal groups on the move. [2] USA Marine Corps Drill and Ceremonies Manual MCO P5060.20 [3] Reeves, W., T., “Particle Systems-A Technique for Modeling a Class of Fuzzy Objects”, ACM Transactions on Graphics, V2–2, April 1983. and reprinted in Computer Graphics. V17–3, July 1983, (ACM SIGGRAPH ’83 Proceedings), pp. 359- 376. [4] Reynolds C. Flocks, herds and schools: a distributed behavioral model. In SIGGRAPH’87: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, ACM, New York, NY, USA, 1987. [5] Reynolds C. Steering behaviors for autonomous characters. In Game Developers Conference, Miller Freeman Game Group, San Francisco, CA, USA, 1999. [6] Reynolds C. Big fast crowds on PS3. In Sandbox’06: Proceedings of the 2006 ACM SIGGRAPH Symposium on Videogames, ACM, New York, NY, USA, 2006. [7] http://opensteer.sourceforge.net/
  • 27. References [2] [8] Balch T, Hybinette M. Social potentials for scalable multirobot formations. In IEEE International Conference on Robotics and Automation (ICRA 2000), San Francisco, 2000. [9] Kamphuis A., Overmars M. H. Motion planning for coherent groups of entities. In IEEE Int. Conf. on Robotics and Automation. IEEE Press, San Diego, CA, 2004. [10] Silveira, R., Prestes, E., and Nedel, L. P. 2008. Managing coherent groups. Comput. Animat. Virtual Worlds 19, 3-4 (Sep. 2008), 295-305. [11] Buckland M. Programming Game AI by Example. Wordware Publishing, 2005. [12] Massive software http://www.massivesoftware.com. Accessed on May 2009. [13] Pro OGRE 3D Programming, (Gregory Junker). [14] R. De Chiara, U. Erra, M. Tatafiore and V. Scarano. Massive simulation using GPU of a distributed behavioral model of a flock with obstacle avoidance. Proceedings of Vision, Modeling, and Visualization 2004 (VMV 2004) (Stanford - California, USA, Nov 16 - 18, 2004). pp. 233-240.

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