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Better Group Behaviors in Complex Environments using Global Roadmaps

O. Burchan Bayazit, Jyh-Ming Lien and Nancy M. Amato. Better Group Behaviors in Complex Environments using Global Roadmaps. Andreas Edlund <andreas.edlund@stanford.edu>. Introduction. Flocks and crowds. Craig Raynolds' “boids”, SIGGRAPH'87

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Better Group Behaviors in Complex Environments using Global Roadmaps

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  1. O. Burchan Bayazit, Jyh-Ming Lien and Nancy M. Amato Better Group Behaviors in Complex Environments using Global Roadmaps Andreas Edlund <andreas.edlund@stanford.edu>

  2. Introduction • Flocks and crowds. • Craig Raynolds' “boids”, SIGGRAPH'87 • Presented a distributed approach to simulate flocks of individuals.

  3. So what's it used for? • Artificial life. • Explores how various lifeforms behave in larger groups. • Animation. • Used in movies and computer games. • Tim Burton's film “Batman Returns” used a modified version of Raynolds' boids to simulate a swarm of bats and a flock of penguins.

  4. This paper • Behaviour: • Homing Behaviour. • Goal Searching Behaviour. • Narrow Passage Behaviour. • Shepherding Behaviour. • Approaches: • Basic potential field. • Grid based A*. • Rule based roadmap.

  5. Boids • Individuals use “boid”-behaviour. • Avoid collision with flockmates. • Match velocity with flockmates. • Stay close to flockmates. Alignment Separation Cohesion

  6. Global behaviour • Global behaviour is simulated using a potential field. Two force vectors used: • Towards the goal. • Away from obstacles. Boid Goal

  7. Various approaches • Problem with local minima. • Two methods to solve this problem: • Grid based A* search. • Finds shortest paths and is relatively fast. • However, we need to recompute a new path every time we have a new goal. • Roadmap. • Precompute a roadmap for the environment and use it for all the queries.

  8. Homing Behaviour • Search the roadmap to find a path to the goal. • Each node on this path is considered a subgoal. • The flock is attracted to the next subgoal instead of the final goal.

  9. Goal Searching Behaviour • Environment is known, the goal is not. • Objective is to find the goal and get everyone to it. • Tries to duplicate ant behaviour. • Ants drop pheromone on paths to indicate the importance of that particular path. • More ants will walk down paths that are considered more important.

  10. Goal Searching Behaviour Ants Goal

  11. Goal Searching Behaviour

  12. Goal Searching Behaviour

  13. Narrow Passage Behaviour • A naive way is to simply use the homing behaviour.

  14. Narrow Passage Behaviour • We'll get problems with congestion though. • It would be better if the ants formed some kind of queue.

  15. Narrow Passage Behaviour • The paper proposes a “follow-the-leader” strategy: • Move to the passage using the homing behaviour. • At the entrance node select the ant closest to the entrance and designate that ant the “leader”. The other ants are “followers”. • The leader's subgoal is the next node in the narrow path. • The other ants line up behind each other and uses the ant in front of him as his subgoal.

  16. Narrow Passage Behaviour

  17. Narrow Passage Behaviour • Select a leader.

  18. Narrow Passage Behaviour • Select the first follower.

  19. Narrow Passage Behaviour • Select the the next follower.

  20. Narrow Passage Behaviour • And so on ...

  21. Shepherding Behaviour • The sheep have boid behaviour. • The sheep dog repels the sheep by a certain amount of force. Dog Goal Sheep

  22. Shepherding Behaviour • The herd is continuously grouped into subgroups based on the sheep's positions. Subgroup Another subgroup

  23. Shepherding Behaviour • Dog always herds the subgroup that is the farthest away from the subgoal. Subgoal

  24. Shepherding Behaviour • Algorithm based on an experiment with actual geese. • From Richard Vaughan, 2000.

  25. Experimental Results • Homing behaviour: • Basic versus grid based A* versus MAPRM. • 301 random obstacles. • 30 s runtime.

  26. Experimental Results • Homing behaviour:

  27. Experimental Results • Goal Searching behaviour: • 16 obstacles occupies 24 % of the environment. • 50 flock members. • Sensory radius: 5 m. • 80 x 100 m environment.

  28. Experimental Results • Narrow passage behaviour: • Naive homing behaviour versus follow-the-leader. • 50 flock members. • One narrow passage between two mountains.

  29. Experimental Results • Narrow passage behaviour:

  30. Experimental Results • Shepherd behaviour: • Grid based A* versus roadmap. • 30 sheep.

  31. Experimental Results • Shepherd behaviour: • Comparison between different strength of the sheep dog's repulsive force.

  32. Conclusions and rants • Roadmap is better than basic and A* (what a surprise). • Faster and few local mimima. • Rants: • Algorithms poorly described. • What's up with the narrow passage experiment? • Escape from local minima?

  33. Further reading • Boids • Craig Raynolds, “Flocks, Herds, and Schools: A Distributed Behavioral Model”, SIGGRAPH'87 • Shepherding • Richard Vaughan, Neil Sumpter, Jane Henderson, Andy Frost and Stephen Cameron, “Experiments in automatic flock control”, 2000

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