Better Group Behaviors in Complex Environments using Global Roadmaps
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Transcript of Better Group Behaviors in Complex Environments using Global Roadmaps
Better Group Behaviors in Complex Environments using Global
Roadmaps
O. Burchan Bayazit, Jyh-Ming Lien and Nancy M. Amato
Andreas Edlund <[email protected]>
Introduction
● Flocks and crowds.● Craig Raynolds' “boids”, SIGGRAPH'87
– Presented a distributed approach to simulate flocks of individuals.
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.
This paper
● Behaviour:– Homing Behaviour.– Goal Searching Behaviour.– Narrow Passage Behaviour.– Shepherding Behaviour.
● Approaches:– Basic potential field.– Grid based A*.– Rule based roadmap.
Boids
● Individuals use “boid”-behaviour.– Avoid collision with flockmates.– Match velocity with flockmates.– Stay close to flockmates.
Separation Alignment Cohesion
Global behaviour
● Global behaviour is simulated using a potential field. Two force vectors used:– Towards the goal.– Away from obstacles.
Goal
Boid
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.
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.
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.
Goal Searching Behaviour
Ants
Goal
Goal Searching Behaviour
Goal Searching Behaviour
Narrow Passage Behaviour
● A naive way is to simply use the homing behaviour.
Narrow Passage Behaviour
● We'll get problems with congestion though.● It would be better if the ants formed some kind of
queue.
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.
Narrow Passage Behaviour
Narrow Passage Behaviour
● Select a leader.
Narrow Passage Behaviour
● Select the first follower.
Narrow Passage Behaviour
● Select the the next follower.
Narrow Passage Behaviour
● And so on ...
Shepherding Behaviour
● The sheep have boid behaviour.● The sheep dog repels the sheep by a certain
amount of force.
Goal
Sheep
Dog
Shepherding Behaviour
● The herd is continuously grouped into subgroups based on the sheep's positions.
Subgroup
Another subgroup
Shepherding Behaviour
● Dog always herds the subgroup that is the farthest away from the subgoal.
Subgoal
Shepherding Behaviour
● Algorithm based on an experiment with actual geese.
● From Richard Vaughan, 2000.
Experimental Results
● Homing behaviour:– Basic versus grid based A* versus MAPRM.– 301 random obstacles.– 30 s runtime.
Method #flockmates reaching goalBasic 10Roadmap 40A* search 40
Experimental Results
● Homing behaviour:
Local minimaMethod Init time (s) Find path time (s) # Escape (s)
Roadmap 0.88 0.65 255 22.99A* search 6.02 5.76 2005 1035.43
Experimental Results
● Goal Searching behaviour:– 16 obstacles occupies 24 % of the environment.– 50 flock members.– Sensory radius: 5 m.– 80 x 100 m environment.
Experimental Results
● Narrow passage behaviour:– Naive homing behaviour versus follow-the-leader.– 50 flock members.– One narrow passage between two mountains.
Experimental Results
● Narrow passage behaviour:
Experimental Results
● Shepherd behaviour:– Grid based A* versus roadmap.– 30 sheep.
Method Init time (s) #steps #local min.Roadmap 0.88 2348.17 7.8A* search 6.02 10612.08 32.2
Experimental Results
● Shepherd behaviour:– Comparison between different strength of the sheep
dog's repulsive force.
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?
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